Dataset_MRI.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KtrfqNCCNLHQ"
      },
      "source": [
        "# Setting Up the Notebook\n",
        "\n",
        "Before proceeding with the creation of the dataset needed for the experiment, we need to install and import some libraries. More specifically, we need to install the Python library that allows to read NIFTI images (the format in which the PET scans are stored)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1bQJqE9_AcOg"
      },
      "outputs": [],
      "source": [
        "!pip install nibabel p_tqdm 2>&1 >/dev/null\n",
        "\n",
        "from p_tqdm import p_uimap\n",
        "import numpy as np\n",
        "import nibabel as nib\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import pandas as pd\n",
        "import sys\n",
        "import re\n",
        "import yaml\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pKcyS96aNLHW"
      },
      "source": [
        "We can then proceed with the mounting of the Google Drive folder, that will be needed later to actually use the data. Here, we've chosen as mountpoint the `/datasets/drive` folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0aop65tBOke",
        "outputId": "e32e1ce0-642b-4182-db9f-7492a24ca3dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /datasets/drive\n"
          ]
        }
      ],
      "source": [
        "!mkdir /datasets -p\n",
        "from google.colab import drive\n",
        "drive.mount('/datasets/drive', force_remount=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uhvO4fWlNLHY"
      },
      "source": [
        "We can now set some variables for ensuring the reproducibility of the experiment and the readability of the code. More specifically, we'll set the path to the mounted Google Drive folder that contains the \"raw\" scans, as well as the random seed and a flag that tells if this is the first run of the notebook or not (so that it won't regenerate unneeded CSV files that may be different for each run)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u1RnhDgmA_O2"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import random\n",
        "from pathlib import Path\n",
        "DATA_DIR = Path(\"Your directory here\")\n",
        "RANDOM_SEED = 1234\n",
        "random.seed(RANDOM_SEED)\n",
        "FIRST_RUN = True"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F3VQG9lvNLHc"
      },
      "source": [
        "# Importing the Data\n",
        "\n",
        "## Importing the CSV data\n",
        "\n",
        "The OASIS 3 dataset provides different CSV files that contain various information about the visits of each patient. Here, we're importing them and we're fixing some of the errors that they contain (e.g., the CSVs implicitly contain information regarding the date and the subject, that we'll make explicit)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sh9bGS6EBZsn"
      },
      "outputs": [],
      "source": [
        "subjects = pd.read_csv(DATA_DIR / 'csv/subjects.csv')\n",
        "clinical_data = pd.read_csv(DATA_DIR / 'csv/clinical-data.csv')\n",
        "pet_data = pd.read_csv(DATA_DIR / 'csv/pet.csv')\n",
        "pup_data = pd.read_csv(DATA_DIR / 'csv/pup.csv')\n",
        "mri_data = pd.read_csv(DATA_DIR / 'csv/mri.csv')\n",
        "\n",
        "# By default, the CSVs do not contain some information explicitly\n",
        "# (although the columns have been defined). Thus, here we set them\n",
        "# to usable values.\n",
        "\n",
        "pup_data['Date'] = (pup_data['PUP_PUPTIMECOURSEDATA ID']\n",
        "                        .apply(lambda s: int(s.split('_')[3][1:])))\n",
        "pup_data['Subject'] = (pup_data['PUP_PUPTIMECOURSEDATA ID']\n",
        "                           .apply(lambda s: s.split('_')[0]))\n",
        "\n",
        "clinical_data['Date'] = (clinical_data['ADRC_ADRCCLINICALDATA ID']\n",
        "                             .apply(lambda s: int(s.split('_')[2][1:])))\n",
        "\n",
        "pet_data['Tracer'] = (pet_data['XNAT_PETSESSIONDATA ID']\n",
        "                          .map(lambda x: x.split('_')[1]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCNBuVpJNLHg"
      },
      "source": [
        "Thus, the composition of the dataset is as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VBF01ErSBDuB",
        "outputId": "1095c7e7-7934-4609-c769-4b4c815a6eed",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of subjects: 1098\n",
            "Number of MRI scans: 2168\n",
            "Number of PET scans: 1607\n",
            "Number of PUP results: 1352\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of subjects:\", subjects.shape[0])\n",
        "print(\"Number of MRI scans:\", mri_data.shape[0])\n",
        "print(\"Number of PET scans:\", pet_data.shape[0])\n",
        "print(\"Number of PUP results:\", pup_data.shape[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cas4ZIV_COlQ",
        "outputId": "51afbe0e-9599-4947-f85a-5eaf91d1190b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "PIB     935\n",
              "AV45    417\n",
              "Name: tracer, dtype: int64"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pup_data['tracer'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l4oxdWQRCS2N"
      },
      "source": [
        "# Labelling the Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8rfH-mybNLHm"
      },
      "source": [
        "The OASIS3 dataset does not contain explicit labels for each PET scan. Thus a labelling phase is needed. We start by using the \"clinical data\" CSV file, that contains at most five differential diagnosis for each visit for each patient. Here, we'll normalize the format by replacing the \"dots\" as empty cells."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YFNsBpmkCUpk"
      },
      "outputs": [],
      "source": [
        "clinical_data.replace({'.': None, np.nan: None}, inplace=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtSh6Hh8NLHn"
      },
      "source": [
        "We can now simplify the diagnosis by mapping each diagnosis to either 1 (meaning that the patient suffers from Alzheimer's or other similar diseases) or 0 (meaning that the patient is healthy or suffers from different diseases)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d1ah5LChCXio"
      },
      "outputs": [],
      "source": [
        "labels = set()  \n",
        "for i in range(1, 6):\n",
        "    labels = labels.union(clinical_data[f'dx{i}'].unique())\n",
        "simplified = {str(k): re.match('^(AD dem|Vasc.*? dem|Frontotemporal dem|other mental retarAD demion|(Active )?DLBD|Active PSNP|Dementia)', str(k), re.I) is not None for k in labels}\n",
        "simplified = {k: v or re.match('^uncertain.*?dem', k) is not None for k, v in simplified.items()}\n",
        "# We classify uncertain cases as sick"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jz4RsE_tNLHo"
      },
      "source": [
        "Thus, by attaching the newly created diagnosis to the original CSV, we obtain the following distribution:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "Uneh_vPQCZP-",
        "outputId": "a4fc6656-21be-4b76-fdf9-259352bb3126",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False    4691\n",
              "True     1533\n",
              "Name: dementia, dtype: int64"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "clinical_data['dementia'] = clinical_data[[f'dx{i}' for i in range(1,6)]].replace(simplified).any(axis=1)\n",
        "clinical_data['dementia'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "clinical_data['dementia'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VPJGAX9JNLHp"
      },
      "source": [
        "It is clear that, since dementia is a spectrum rather than a symptom, there may be some \"uncertain\" cases:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "a04X0ejpCfpx",
        "outputId": "c54ae7a7-7e3c-4937-ae22-40f182e36fa0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "subj = 'OAS30040'\n",
        "(clinical_data.groupby('Subject')\n",
        "    .get_group(subj)\n",
        "    .replace({True: 1, False: 0})\n",
        "    .plot.scatter(x='Date', y='dementia', title=subj, figsize=(8,4)))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N5khX1cxNLHq"
      },
      "source": [
        "We thus apply a simple algorithm that tries to find and correct any such cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kcWGufqGCisI"
      },
      "outputs": [],
      "source": [
        "clinical_data = clinical_data.sort_values(by=['Subject', 'Date']).reset_index(drop=True)\n",
        "current_subj = None\n",
        "for index, row in clinical_data.iterrows():\n",
        "    if current_subj != row['Subject']:\n",
        "        current_subj = row['Subject']\n",
        "        prec = (None, None) # i-2, i-1\n",
        "        succ = (clinical_data.at[index + 1, 'dementia'] if index + 1 in clinical_data.index and clinical_data.at[index + 1, 'Subject'] == current_subj else None, clinical_data.at[index + 2, 'dementia'] if index + 2 in clinical_data.index and clinical_data.at[index + 2, 'Subject'] == current_subj else None)\n",
        "    \n",
        "    if row['dementia'] == True:\n",
        "        new = prec[1] or any(succ)\n",
        "    else:\n",
        "        new = any(prec) and any(succ)\n",
        "    clinical_data.at[index, 'dementia'] = new\n",
        "    \n",
        "    prec = (prec[1], new) # i-1, i\n",
        "    succ = (succ[1], clinical_data.at[index + 3, 'dementia'] if index + 3 in clinical_data.index and clinical_data.at[index + 3, 'Subject'] == current_subj else None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_emYlrl-NLHq"
      },
      "source": [
        "Applying this algorithm, the previous example becomes as follows (thus all false negatives have been fixed)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "SirHZeKiClb5",
        "outputId": "b8aee6f1-241f-4135-f8c2-790dd40f99b9",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "subj = 'OAS30040'\n",
        "(clinical_data.groupby('Subject')\n",
        "    .get_group(subj)\n",
        "    .replace({True: 1, False: 0})\n",
        "    .plot.scatter(x='Date', y='dementia',title=subj, figsize=(8,4)))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OorTwcTZNLHr"
      },
      "source": [
        "We can finally attach a label to each PET scan by associating the label of the nearest visit in time."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sGLIdGGECn2L"
      },
      "outputs": [],
      "source": [
        "labeled_dataset = pup_data.copy()\n",
        "labeled_dataset.drop(columns=['procType', 'model', 'templateType', 'FSId', 'mocoError', 'regError', 'Centil_fBP_TOT_CORTMEAN', 'Centil_fSUVR_TOT_CORTMEAN', 'Centil_fBP_rsf_TOT_CORTMEAN', 'Centil_fSUVR_rsf_TOT_CORTMEAN'], inplace=True)\n",
        "labeled_dataset['Label'] = None\n",
        "for index, row in labeled_dataset.iterrows():\n",
        "    rows_of_subject = clinical_data.loc[clinical_data['Subject'] == row['Subject']][['Date', 'dementia']]\n",
        "    rows_of_subject['Date'] = rows_of_subject['Date'].apply(lambda x: abs(x - row['Date']))\n",
        "    labeled_dataset.at[index,'Label'] = (rows_of_subject.groupby(by='Date', as_index=False) \n",
        "                                                        .any()\n",
        "                                                        .sort_values(by='Date')\n",
        "                                                        .loc[0]['dementia'])\n",
        "    \n",
        "labeled_dataset.to_csv(DATA_DIR / 'csv' / 'labeled_dataset.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gb6vIy_ECsGm"
      },
      "source": [
        "## Dealing with Class Imbalance\n",
        "\n",
        "By plotting the composition of the dataset, it is clear that we're working with an heavily imbalanced dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "YypS0nJfCq3e",
        "outputId": "c52eeef1-bb30-419e-b5a4-7d78b9c23409",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False    0.900148\n",
              "True     0.099852\n",
              "Name: Label, dtype: float64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "labeled_dataset['Label'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "labeled_dataset['Label'].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zsFQOkr6Kl-7",
        "outputId": "3ea81f97-ab0a-46d5-8de4-78a8b6fdd780"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1352, 6)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labeled_dataset.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-i7w17oXNLHt"
      },
      "source": [
        "To fix this issue, we apply a combination of random undersampling on negative samples and data augmentation on positive samples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l1zyOCnC1W-N"
      },
      "source": [
        "### Grouping scans by subject"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F053V6RP1kXN"
      },
      "source": [
        "Since many scans are associated to a single subject, in order to avoid future bias at training time, we decide to take only one scan for each subject. More specifically, we take the most recent scan take for a patient."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jA1yH6ct4wzi",
        "outputId": "7ba9020a-9b40-4b39-e8e1-5b38fd6c6b01"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False    618\n",
              "True     112\n",
              "Name: Label, dtype: int64"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_dataset = labeled_dataset.sort_values(['Subject','Date'], ascending=[True,True])\n",
        "new_dataset.drop_duplicates(subset=['Subject'], keep='last', inplace=True)\n",
        "new_dataset['Label'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpY4fAUZCw-2"
      },
      "source": [
        "### Random Undersampling\n",
        "\n",
        "We randomly select a subset of negative samples, by making sure that we try and keep as most data on the positive patients as possible. To ensure reproducibility, we save the selected negative samples to a CSV."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FxOWN3hPCu6M"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "    positive = new_dataset.loc[labeled_dataset['Label'] == True]\n",
        "    positive.to_csv(DATA_DIR / 'csv' / 'new-positive.csv')\n",
        "    negative = new_dataset.loc[labeled_dataset['Label'] == False]\n",
        "    negative = negative.sample(n = 150, random_state=RANDOM_SEED)\n",
        "    negative.sort_values(['Subject'], ascending=[True], inplace=True)\n",
        "    negative.to_csv(DATA_DIR / 'csv' / 'new-negative.csv')\n",
        "\n",
        "    sampled = pd.concat([positive, negative])\n",
        "    sampled.to_csv(DATA_DIR / 'csv' /'new_sample.csv')\n",
        "else:\n",
        "    sampled = pd.concat([pd.read_csv(DATA_DIR / 'csv/new-positive.csv'), pd.read_csv(DATA_DIR / 'csv/new-negative.csv')])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzxDI1TzNLH2"
      },
      "source": [
        "The modified dataset composition becomes as follows:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "id": "n__-T63UC1X8",
        "outputId": "a349a419-6c65-4190-894f-8cc444d07731"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "False    150\n",
              "True     112\n",
              "Name: Label, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sampled['Label'].value_counts().plot.pie(autopct='%1.1f%%')\n",
        "sampled['Label'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nc3eZ_Lv_bdb"
      },
      "source": [
        "We need to trace the corresponding MRI for each of the selected PET"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qPsyw6J7g4xW"
      },
      "source": [
        "To obtain the same balancement of the PET's dataset, it is necessary to augment positive MRIs, until both of them reach 150 occurencies."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVJAVXJUC3Oh"
      },
      "source": [
        "### Data augmentation\n",
        "\n",
        "To further reduce the unbalancing of the dataset, we augment the positive samples through the means of random rotations and flips."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hq-amj_zC4Y0"
      },
      "outputs": [],
      "source": [
        "from scipy.ndimage.interpolation import rotate\n",
        "\n",
        "# This rotation fills the empty space in the corners with the mean of colours from the corner-patch\n",
        "def rotate_img(img, angle, bg_patch=(5, 5)):\n",
        "    assert len(img.shape) <= 3, \"Incorrect image shape\"\n",
        "    rgb = len(img.shape) == 3\n",
        "    if rgb:\n",
        "        bg_color = np.mean(img[: bg_patch[0], : bg_patch[1], :], axis=(0, 1))\n",
        "    else:\n",
        "        bg_color = np.mean(img[: bg_patch[0], : bg_patch[1]])\n",
        "    img = rotate(img, angle, reshape=False)\n",
        "    mask = [img <= 0, np.any(img <= 0, axis=-1)][rgb]\n",
        "    img[mask] = bg_color\n",
        "    return img\n",
        "\n",
        "\n",
        "def transform(img, fn):\n",
        "    transformed = img.copy()\n",
        "    for f in fn.split(\"|\"):\n",
        "        grp = re.match(r\"^rotate:(-?\\d+?)$\", f)\n",
        "        if grp:\n",
        "            angle = int(grp.group(1))\n",
        "            for i in range(transformed.shape[0]):\n",
        "                transformed[i, :, :] = rotate_img(transformed[i, :, :], angle)\n",
        "        elif f == \"flipv\":\n",
        "            transformed = transformed[:, ::-1, :]\n",
        "        elif f == \"fliph\":\n",
        "            transformed = transformed[:, :, ::-1]\n",
        "        else:\n",
        "            raise ValueError(f\"Illegal 'fn' value '{fn}'\")\n",
        "    return transformed"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4UApzxo-it4n"
      },
      "source": [
        "First let's analyze the content of each MRI folder:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8kS3yIBzitXf"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "  !ls \"$DATA_DIR\"/mri/all/*/* | sed -n 's/^.*_ses-d...._\\(.*\\)\\.json/\\1/p' | sort | uniq -c"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F9Ur2ZefiOuv"
      },
      "source": [
        "Since there are several scans for each MRI, all saved in different subdirectories, we need to extract all of them into the MRI root directory"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l7455Npwikr-"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "  !ls \"$DATA_DIR\"/mri/all/ | xargs -I% sh -c 'mv \"$DATA_DIR\"/mri/all/%/anat*/* \"$DATA_DIR\"/mri/all/%/'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qbjXUISAiqJu"
      },
      "source": [
        "Some scan could have been repeated several times: for this reason we take the last version of the T1W for each folder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9En5o-0foFxV"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "  !ls \"$DATA_DIR\"/mri/all | xargs -I% sh -c 'mkdir \"$DATA_DIR\"/mri/all/%/saved'\n",
        "  !ls \"$DATA_DIR\"/mri/all | xargs -I% sh -c 'mkdir \"$DATA_DIR\"/mri/all/%/discarded'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7ZtkSe-WionX"
      },
      "outputs": [],
      "source": [
        "import shutil\n",
        "\n",
        "if FIRST_RUN: \n",
        "  mri_folders = os.listdir(DATA_DIR/'mri' / 'all')\n",
        "  to_save_t1 = ''\n",
        "  base_t1 = ''\n",
        "  for p in mri_folders:\n",
        "    current = os.listdir(DATA_DIR/'mri'/'all'/p)\n",
        "    print('Listing',p)\n",
        "    max_t1 = 1\n",
        "    for f in current:\n",
        "      print('\\tCurrent:',f)\n",
        "      if \"run\" in f and \"T1w\" in f and \"echo\" not in f:\n",
        "        if max_t1 < int((f.split(\"run\")[1])[2]):\n",
        "          max_t1 = int((f.split(\"run\")[1])[2])\n",
        "          to_save_t1 = f.split('.')[0]\n",
        "      elif \"T1w\" in f and \"echo\" not in f:\n",
        "        base_t1 = f.split('.')[0]\n",
        "    if max_t1 > 1:\n",
        "      print('Copying', to_save_t1)\n",
        "      nii = to_save_t1 + \".nii.gz\"\n",
        "      json = to_save_t1 + \".json\"\n",
        "      shutil.copy(DATA_DIR/'mri'/'all'/p/nii, DATA_DIR/'mri'/'all'/p/'saved')\n",
        "      shutil.copy(DATA_DIR/'mri'/'all'/p/json, DATA_DIR/'mri'/'all'/p/'saved')\n",
        "    else:\n",
        "      nii = base_t1 + \".nii.gz\"\n",
        "      json = base_t1 + \".json\"\n",
        "      print('Copying', base_t1)\n",
        "      shutil.copy(DATA_DIR/'mri'/'all'/p/nii, DATA_DIR/'mri'/'all'/p/'saved')\n",
        "      shutil.copy(DATA_DIR/'mri'/'all'/p/json, DATA_DIR/'mri'/'all'/p/'saved')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4aogAjkmBiB"
      },
      "source": [
        "We then move every file but the last T1W in a *discared* folder."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v30zXc1KmASG"
      },
      "outputs": [],
      "source": [
        "import shutil\n",
        "\n",
        "mri_folders = os.listdir(DATA_DIR/ 'mri' / 'all')\n",
        "for p in mri_folders:\n",
        "  saved = os.listdir(DATA_DIR/'mri'/ 'all' /p/ 'saved')\n",
        "  current = os.listdir(DATA_DIR/'mri'/ 'all' /p)\n",
        "  if len(os.listdir(DATA_DIR/'mri'/'all'/p/'discarded')) != 0:\n",
        "    for f in current:\n",
        "      if f not in saved and ('saved' not in f and 'discarded' not in f):\n",
        "        print('Moving',f,'of',p)\n",
        "        shutil.move(str(DATA_DIR / 'mri'/ 'all' / p / f), str(DATA_DIR / 'mri'/ 'all' / p / 'discarded'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xSzvRlKKJnf7"
      },
      "outputs": [],
      "source": [
        "! rm -r \"$DATA_DIR\"/mri/positive/**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JzuoawFkoVGd"
      },
      "source": [
        "Now we divide the scans into two subfolders *positive* and *negative*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhHJCi2Nob9z",
        "outputId": "509884d9-6d10-4e3c-e4a2-ac25f002fda1"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "  sampled = pd.read_csv(DATA_DIR/'csv'/'new_sample.csv')\n",
        "  pos = sampled['MRId'].loc[sampled['Label'] == True].values\n",
        "  neg = sampled['MRId'].loc[sampled['Label'] == False].values\n",
        "\n",
        "  for p in pos:\n",
        "    !cp -r \"$DATA_DIR\"/'mri/all'/\"$p\" \"$DATA_DIR\"/mri/positive/\n",
        "  for n in neg:\n",
        "    !cp -r \"$DATA_DIR\"/'mri/all'/\"$n\" \"$DATA_DIR\"/mri/negative/"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kKu__rCpNLH8"
      },
      "source": [
        "To ensure reproducibility, we save in a CSV a list of the randomly selected images that will be modified, as well as the ordered list of operations that have been applied."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MyLNlfmnC75N"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "    mri_images = [str(s) for s in DATA_DIR.glob('mri/positive/**/saved/*.nii.gz')]\n",
        "    mri_paths = random.sample(mri_images, k=43)\n",
        "    mod_mri_df = pd.DataFrame(columns=['Date', 'Subject', 'MRId', 'Label'], index=np.arange(0,len(mri_paths)))\n",
        "    sampled = pd.read_csv(DATA_DIR / 'csv' / 'new_sample.csv')\n",
        "\n",
        "    for i, path in enumerate(mri_paths):\n",
        "        # Need to store both the .nii img and the one with mean,\n",
        "        # so we can use the .nii for saving later \n",
        "        nii_img = nib.load(path)\n",
        "        img = nii_img.dataobj\n",
        "        transformation_functions = random.choices(['rotate', 'flipv', 'fliph'],\n",
        "                                            weights=[0.5, 0.25, 0.25],\n",
        "                                            k=random.randint(1,5))\n",
        "        transformation_functions = [s + f':{random.randrange(-30, 30)}' if s == 'rotate' else s for s in transformation_functions]\n",
        "        transform_string = '|'.join(transformation_functions)\n",
        "        mod_mri_df.loc[i] = [str(Path(path).parent.parent).split('_d')[1], str(Path(path).parent.parent).split('/')[-1].split('_')[0], str(Path(path).parent.parent).split('/')[-1], transform_string]\n",
        "        # Apply the transformation function(s)\n",
        "        transformed_img = transform(np.array(img),transform_string)\n",
        "        to_save_img = nib.Nifti1Image(transformed_img, nii_img.affine)\n",
        "        # Save the new generated image (as: ORIGINALE_modID.4dfp.img)\n",
        "        img_name = path.split('/')[-1].split('.')[0]\n",
        "        nib.save(to_save_img, DATA_DIR / 'mri' / 'modified' / (img_name + f'_mod{i}.4dfp.nii'))\n",
        "    \n",
        "    mod_mri_df.to_csv(DATA_DIR/'csv'/'modified-mri.csv', index='False')\n",
        "    \n",
        "else:\n",
        "    mod_mri_df = pd.read_csv(DATA_DIR / 'csv' / 'modified-mri.csv')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pVfcWDPKDSUB"
      },
      "source": [
        "# Preparing the data\n",
        "\n",
        "## Resizing the Images\n",
        "\n",
        "To resize the images to a common size, we'll compute a bounding box of the brain (in order to center it) and we'll select the 20 middle slices of each PET scan. Thus, we start by defining a function to compute the bounding box using the Otsu threshold method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1Jt1x-mtDRpg"
      },
      "outputs": [],
      "source": [
        "def find_brain_bounding_box(image):\n",
        "    x,y,w,h = (image.shape[0]+1, image.shape[1]+1, -1, -1)\n",
        "    for slice in image.T:\n",
        "        blurred = cv2.GaussianBlur(slice.T, (51, 51), 50)\n",
        "        blurred = blurred.astype(np.float64)\n",
        "        blurred *= 255.0 / blurred.max()\n",
        "        blurred[np.where(blurred < 0)] = 0\n",
        "        blurred = blurred.astype(np.uint8)\n",
        "        ret, thres = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
        "        blurred_thres = cv2.medianBlur(thres, 51)\n",
        "        contours, hierarchy = cv2.findContours(blurred_thres, 1, cv2.CHAIN_APPROX_SIMPLE )\n",
        "        curr_x, curr_y, curr_w, curr_h = cv2.boundingRect(contours[0])\n",
        "        w = max(w, curr_x + curr_w)\n",
        "        h = max(h, curr_y + curr_h)\n",
        "        x = min(x, curr_x)\n",
        "        y = min(y, curr_y)\n",
        "    # provide a small padding around the found box:\n",
        "    x = max(x - 10, 0)\n",
        "    y = max(y - 10, 0)\n",
        "    w = min(w + 10, image.shape[0])\n",
        "    h = min(h + 10, image.shape[1])\n",
        "    return x, y, w, h"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8D_jTDgHNLH-"
      },
      "source": [
        "We then define a function to normalize the images"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mT1ePiiwDXwf"
      },
      "outputs": [],
      "source": [
        "def normalize(volume):\n",
        "    \"\"\"Normalize the volume\"\"\"\n",
        "    volume = np.array(volume)\n",
        "    min = volume.min()\n",
        "    max = volume.max()\n",
        "    volume = (volume - min) / (max - min)\n",
        "    volume = volume.astype(\"float32\")\n",
        "    return volume"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d90PYotZNLH_"
      },
      "source": [
        "And we define a function to resize the images to $(128, 128, 50)$ voxels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IjvQWjsWDZyI"
      },
      "outputs": [],
      "source": [
        "def resize_to_input_shape(img):\n",
        "    shape = (128, 128)\n",
        "    n_frames = 50\n",
        "    # Starting index to extract the 50 central frames of the third dimension\n",
        "    index = int(img.shape[2] / 2) - int(n_frames / 2)\n",
        "    final_img = img[:,:,index : index + n_frames]\n",
        "    x, y, w, h = find_brain_bounding_box(final_img)\n",
        "    square_size = max(w - x, h - y)\n",
        "    to_square = square_size - w + x, square_size - h + y\n",
        "    miss_x, miss_y = to_square\n",
        "    x, y = max(int(x - np.floor(miss_x / 2)), 0), max(int(np.floor(y - miss_y / 2)), 0)\n",
        "    w, h = int(np.floor(w + miss_x / 2)), int(np.floor(h + miss_y / 2))\n",
        "    return np.array(\n",
        "        [\n",
        "            #cv2.resize(frame, shape)\n",
        "            cv2.resize(frame[x:w,y:h], shape)\n",
        "            for frame in final_img.T\n",
        "        ]\n",
        "    ).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcugth_vNLH_"
      },
      "source": [
        "Finally, we can define a function that applies all the needed transformations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EvG9-SbTDbwu"
      },
      "outputs": [],
      "source": [
        "def process_scan(path):\n",
        "    \"\"\"Read and resize volume\"\"\"\n",
        "    try:\n",
        "        # Read scan\n",
        "        volume = nib.load(path).get_fdata()\n",
        "        if len(volume.shape) == 4:\n",
        "          volume = np.mean(volume, axis=3)\n",
        "        # Normalize\n",
        "        volume = normalize(volume)\n",
        "        # Resize width, height and depth\n",
        "        volume = resize_to_input_shape(volume)\n",
        "        return volume\n",
        "    except OSError:\n",
        "        print(\"Error in reading\", path, \"(file could be damaged)\", file=sys.stderr)\n",
        "        return None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_pRSCDRNLIA"
      },
      "source": [
        "We can then load a list of all the PET scans that we'll be loaded"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z1a4ZyevDgkO"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "if FIRST_RUN:\n",
        "  pos_mri = [str(s) for s in DATA_DIR.glob('mri/positive/**/saved/*.nii.gz')]\n",
        "  neg_mri = [str(s) for s in DATA_DIR.glob('mri/negative/**/saved/*.nii.gz')]\n",
        "  mod_mri = [str(s) for s in DATA_DIR.glob('mri/modified/*.nii')]\n",
        "  # Saving the paths of every scans in text file for faster reading\n",
        "\n",
        "  pos_paths = open(DATA_DIR/'csv'/'pos_paths.txt','wb')\n",
        "  pickle.dump(pos_mri,pos_paths)\n",
        "  pos_paths.close()\n",
        "\n",
        "  neg_paths = open(DATA_DIR/'csv'/'neg_paths.txt','wb')\n",
        "  pickle.dump(neg_mri,neg_paths)\n",
        "  neg_paths.close()\n",
        "\n",
        "  mri_mod_paths = open(DATA_DIR/'csv'/'mri_mod_paths.txt','wb')\n",
        "  pickle.dump(mod_mri,mri_mod_paths)\n",
        "  mri_mod_paths.close()\n",
        "else:\n",
        "  pos_mri = pickle.load(open(DATA_DIR/'csv'/'pos_paths.txt','rb'))\n",
        "  neg_mri = pickle.load(open(DATA_DIR/'csv'/'neg_paths.txt','rb'))\n",
        "  mod_mri = pickle.load(open(DATA_DIR/'csv'/'mri_mod_paths.txt','rb'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XEry3fT0JUIV"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "pos_mri = pickle.load(open(DATA_DIR/'csv'/'pos_paths.txt','rb'))\n",
        "neg_mri = pickle.load(open(DATA_DIR/'csv'/'neg_paths.txt','rb'))\n",
        "mod_mri = pickle.load(open(DATA_DIR/'csv'/'mri_mod_paths.txt','rb'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izHyGv3gNLIB"
      },
      "source": [
        "We can print the availability of the scans (note that the dataset is balanced as the modified images are only positive)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSXe3JYJc5ll",
        "outputId": "13020a40-049a-4d55-df23-1c53f03cdf24",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of positive MRI: 148\n",
            "Total number of negative MRI: 148\n"
          ]
        }
      ],
      "source": [
        "print(\"Total number of positive MRI:\",(len(pos_mri) + len(mod_mri)))\n",
        "print(\"Total number of negative MRI:\",(len(neg_mri)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSx_zmuBNLIB"
      },
      "source": [
        "Before applying the processing functions to all the images in batch, we can test the resizing function on a single image to check if everything works as desired."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "77hLZLVvYrTu",
        "outputId": "d08246e7-5fcf-4d90-ce33-a7caaaff456c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(176, 240, 50)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "path = pos_mri[-2]\n",
        "pos = np.array(nib.load(path).dataobj)\n",
        "r_pos = process_scan(path)\n",
        "fig,ax = plt.subplots(1,3, figsize=(8,8))\n",
        "plt.savefig(DATA_DIR/'original.png', bbox_inches='tight')\n",
        "ax[0].imshow(pos[:,:,int(pos.shape[2]/2)])\n",
        "ax[0].set_title('original')\n",
        "index = int(pos.shape[2] / 2) - int(50 / 2)\n",
        "sliced = pos[:,:,index : index + 50]\n",
        "print(sliced.shape)\n",
        "rect = find_brain_bounding_box(sliced)\n",
        "plt.savefig(DATA_DIR/'bounding.png', bbox_inches='tight')\n",
        "ax[1].imshow(cv2.rectangle(normalize(sliced[:,:,int(sliced.shape[2]/2)]), (rect[0],rect[1]), (rect[2],rect[3]), 1, 2).get())\n",
        "ax[1].set_title('bounding box')\n",
        "plt.savefig(DATA_DIR/'resized.png', bbox_inches='tight')\n",
        "ax[2].imshow(r_pos[:,:,25])\n",
        "ax[2].set_title('resized')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "TkI-MyeOjNlt",
        "outputId": "11ba32c9-8c68-4701-8085-7778ca2eb8c5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f21ca22b950>"
            ]
          },
          "execution_count": 87,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "img = pos[:,:,25]\n",
        "blurred = cv2.GaussianBlur(img, (51, 51), 50)\n",
        "blurred = blurred.astype(np.float64)\n",
        "blurred *= 255.0 / blurred.max()\n",
        "blurred[np.where(blurred < 0)] = 0\n",
        "blurred = blurred.astype(np.uint8)\n",
        "ret, thres = cv2.threshold(blurred, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n",
        "blurred_thres = cv2.medianBlur(thres, 51)\n",
        "plt.imshow(blurred_thres, cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "exodV6q_lQTc",
        "outputId": "ce7328b6-4531-44e4-bcc4-4f8b98ba628d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "im = pos[:,:,int(pos.shape[2]/2)]\n",
        "imS = cv2.resize(im, (128,128))\n",
        "plt.imshow(imS)\n",
        "plt.savefig(DATA_DIR/'original.pgf')\n",
        "blurred_thres = cv2.resize(blurred_thres, (128, 128))\n",
        "im = blurred_thres\n",
        "imS = cv2.resize(im, (128,128))\n",
        "plt.imshow(imS, cmap='gray')\n",
        "plt.savefig(DATA_DIR/'otsu.pgf')\n",
        "im = cv2.rectangle(normalize(sliced[:,:,int(sliced.shape[2]/2)]), (rect[0],rect[1]), (rect[2],rect[3]), 1, 2).get()\n",
        "imS = cv2.resize(im, (128, 128))\n",
        "plt.imshow(imS)\n",
        "plt.savefig(DATA_DIR/'bounding.pgf')\n",
        "im = r_pos[:,:,25]\n",
        "imS = cv2.resize(im, (128, 128))\n",
        "plt.imshow(imS)\n",
        "plt.savefig(DATA_DIR/'resized.pgf')\n",
        "im = r_pos[:,:,25]\n",
        "imS = cv2.resize(im, (128, 128))\n",
        "plt.imshow(imS,cmap='gray')\n",
        "plt.savefig(DATA_DIR/'final.pgf')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "SoTrUoP_6WMs",
        "outputId": "15d4e53b-51c9-4890-d2d9-487e7450c8e7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(176, 240, 256)\n",
            "(128, 128, 50)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 3 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_img = np.array(nib.load(pos_mri[-2]).dataobj)\n",
        "print(test_img.shape)\n",
        "r_pos = process_scan(pos_mri[0])\n",
        "print(r_pos.shape)\n",
        "fig,ax = plt.subplots(1,3, figsize=(8,8))\n",
        "ax[0].imshow(r_pos[:,:,25])\n",
        "ax[0].set_title('middle scan')\n",
        "ax[1].imshow(r_pos[:,:,0])\n",
        "ax[1].set_title('first scan')\n",
        "ax[2].imshow(r_pos[:,:,49])\n",
        "ax[2].set_title('last scan')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gg2uzhHINLIC"
      },
      "source": [
        "We can now load and process all the images."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "b0a96062c3914ff2bed6575395b7d435",
            "9842ee9085464e1cb06fc5935af81267",
            "1896ab78d96144dd8a770aaef74bc1c2",
            "cb778c1e494f4326a0ab90febf249609",
            "f004a44a35ef439f9818d3b5575310e9",
            "a5fe6a4dc9c447b1bc36fc7014018a5f",
            "2ed01ab9ca274811803d7551d01a41e5",
            "7acf87a23e874530abafffcd121b08f1",
            "4bd3a9b18b564feeaecfe6b37866e7c8",
            "35251cf6788e4d4fb4c7f0703f9e3988",
            "5c087c5aad704e168d71c61631cc3149",
            "6b0f8149a954454394fdd8d186aeea72",
            "0bbde9abc5bd4180b9d3af77084df07a",
            "7419177e6fa54d1d9d42c50bc4869141",
            "2acab8e12e954af9973f78f7f8e74ac9",
            "89feff38c0f249d9be4f2923416effad",
            "e7041f0f66ef451bab04164ee73b011c",
            "dc063062cec141c4b8b4ca66a5d2a604",
            "5cb9b1a7a1ac45e2991fadc627785b42",
            "b097bfda7dcc4799a31d669242f5ad2a",
            "50624d9ac7874d78a7650520934922bf",
            "4909d41e569d4135a9b7ee3295cc0e07"
          ]
        },
        "id": "KOSgRxKYDwcO",
        "outputId": "f703eb88-7849-499c-bce9-5d817928d3ac"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b0a96062c3914ff2bed6575395b7d435",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/205 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6b0f8149a954454394fdd8d186aeea72",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/205 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "pos = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] ==20, p_uimap(process_scan, pos_images + pos_mod_images))))\n",
        "pos_label = np.ones(len(pos)*20, dtype=np.uint8)\n",
        "neg = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] == 20, p_uimap(process_scan, neg_images + neg_mod_images))))\n",
        "neg_label = np.zeros(len(neg)*20, dtype=np.uint8)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbhaT4hUNLIC"
      },
      "source": [
        "We can then transform the 3D images to a sequence of 2D images (to reduce the required computing power in the training phase)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GtYp66NS3Xlh",
        "outputId": "2936cadb-bdce-4a94-87f4-4ec0c69fddf8",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(8200, 128, 128)\n"
          ]
        }
      ],
      "source": [
        "x = np.concatenate((*np.concatenate((pos, neg), axis=0),), axis = 2).T\n",
        "print(x.shape)\n",
        "y = np.concatenate((pos_label, neg_label), axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QbH5Ja_7NLIC"
      },
      "source": [
        "To check if everything worked as expected, the size of the list of images and the list of labels should be the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYYFjJl78dtQ",
        "outputId": "45b63c9e-8913-40b1-afd8-aaa575c8f2b3",
        "scrolled": true
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(8200, 128, 128) (8200,)\n"
          ]
        }
      ],
      "source": [
        "print(x.shape, y.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPdDv63HNLID"
      },
      "source": [
        "Furthermore a single image should have a size of $128\\times 128$ pixels."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V5Qlv1E_CDou",
        "outputId": "332ca93d-d881-41a4-e67a-f8446ed134a4",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(128, 128)"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "x[0].shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "adS-7TI-NLID"
      },
      "source": [
        "We can plot an example image to check it."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "_Xj0NtLOBvr_",
        "outputId": "6a1646e9-ac0e-431e-fba1-428496afc3af",
        "scrolled": true
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7fb6bb4a2a90>"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.imshow(x[1])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itdw30gENLIE"
      },
      "source": [
        "## Generating the CV Folds\n",
        "\n",
        "To reduce the required time in later phases, we can pre-compute the cross-validation folds. We start by splitting the dataset into a train-test set (using the 20% as test split). Note that in the following code the \"test set\" is referred to as \"validation set\" due to a coding error: sadly, due to the size of the final dataset, it is pretty expensive to move and rename the folder and its files."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fmBgpmHW_JwC"
      },
      "outputs": [],
      "source": [
        "if FIRST_RUN:\n",
        "  mod_mri = pd.read_csv(DATA_DIR / 'csv' / 'modified-mri.csv')\n",
        "  mod_mri = mod_mri[['Date','Subject','MRId','Label']]\n",
        "  #mod_mri.rename(columns={'Transform':'Label'}, inplace=True)\n",
        "  mod_mri['Label'] = True\n",
        "  mod_mri.to_csv(DATA_DIR / 'csv' / 'modified-mri.csv', index=False)\n",
        "else:\n",
        "  mod_mri = pd.read_csv(DATA_DIR / 'csv' / 'modified-mri.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f45VHfLNjs3x"
      },
      "outputs": [],
      "source": [
        "pos = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] ==20, p_uimap(process_scan, pos_images + pos_mod_images))))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQWirU7VsrtP",
        "outputId": "b0d48cf1-8c7b-4abc-d30e-c3e4bb168b8b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "False    118\n",
            "True      75\n",
            "Name: Label, dtype: int64\n",
            "True     118\n",
            "False    118\n",
            "Name: Label, dtype: int64\n",
            "True     30\n",
            "False    30\n",
            "Name: Label, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "sample = pd.read_csv(DATA_DIR /'csv/new_sample.csv').drop(columns=['tracer','PUP_PUPTIMECOURSEDATA ID'])\n",
        "sample = sample[sample['MRId'].map(lambda x: x not in ['OAS30031_MR_d0236', 'OAS30078_MR_d0136', 'OAS30144_MR_d1708', 'OAS30650_MR_d1620', 'OAS31135_MR_d0096', 'OAS31140_MR_d0148', 'OAS31171_MR_d0441', 'OAS30058_MR_d0836', 'OAS30297_MR_d2331'])]\n",
        "sample = sample.reset_index()\n",
        "train_pos = sample[:75]\n",
        "test_pos = sample[75:105]\n",
        "train_neg = sample[105:223]\n",
        "test_neg = sample[223:]\n",
        "trainset = pd.concat([train_pos,train_neg])\n",
        "print(trainset['Label'].value_counts())\n",
        "trainset = pd.concat([mod_mri,trainset], ignore_index=True)\n",
        "print(trainset['Label'].value_counts())\n",
        "testset = pd.concat([test_pos,test_neg], ignore_index=True)\n",
        "print(testset['Label'].value_counts())\n",
        "xtrain = trainset\n",
        "xtrain.replace({False: 0, True: 1}, inplace=True)\n",
        "ytrain = xtrain['Label'].to_numpy()\n",
        "xtrain.drop(columns=['Label','Date','Subject'], inplace=True)\n",
        "xtrain = xtrain['MRId'].to_numpy()\n",
        "np.save(DATA_DIR/'mri'/'subjects_xtrain.npy', xtrain)\n",
        "np.save(DATA_DIR/'mri'/'subjects_ytrain.npy', ytrain)\n",
        "\n",
        "xtest = testset\n",
        "xtest.replace({False: 0, True: 1}, inplace=True)\n",
        "ytest = xtest['Label'].to_numpy()\n",
        "xtest.drop(columns=['Label','Date','Subject'], inplace=True)\n",
        "xtest = xtest['MRId'].to_numpy()\n",
        "np.save(DATA_DIR/'mri'/'subjects_xtest.npy', xtest)\n",
        "np.save(DATA_DIR/'mri'/'subjects_ytest.npy', ytest)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Iq9BlgrQrL2I"
      },
      "outputs": [],
      "source": [
        "np.save(DATA_DIR/'mri'/'ytest.npy', ytest)\n",
        "np.save(DATA_DIR/'mri'/'ytrain.npy', ytrain)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u8BbWuJ2b3_c"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "list_pos_mri = pickle.load(open(DATA_DIR/'csv'/'pos_paths.txt','rb'))\n",
        "list_neg_mri = pickle.load(open(DATA_DIR/'csv'/'neg_paths.txt','rb'))\n",
        "list_mod_mri = pickle.load(open(DATA_DIR/'csv'/'mri_mod_paths.txt','rb'))\n",
        "\n",
        "mri = list_pos_mri + list_neg_mri + list_mod_mri"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZ5mLeh5hTgB"
      },
      "outputs": [],
      "source": [
        "xtrain = np.load(DATA_DIR/'mri/subjects_xtrain.npy', allow_pickle=True)\n",
        "xtest = np.load(DATA_DIR/'mri/subjects_xtest.npy', allow_pickle=True)\n",
        "mri = dict((map(lambda s: (re.sub(r'^.*/sub-(.*?)_ses-(d\\d+?)_.*?\\.nii(?:\\.gz)?',r'\\1_MR_\\2',s),s), mri)))\n",
        "train_paths = list(map(lambda x: mri[x] , xtrain))\n",
        "test_paths = list(map(lambda x: mri[x] , xtest))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "3f66014e407a41a487f689582e04c56e",
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            "e522cdbf82cc4d54b3aad6e7f3ddc65c",
            "a1a73f221fa840ad84aec7dc7d6f7913",
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            "2b28d1d250ca4454828f1dcc1437c94d",
            "818eb5607e9b4e82a9832968f6d875cb"
          ]
        },
        "id": "lOMH6HMDc13z",
        "outputId": "e4bfbdb9-9158-4f3a-a749-e7b948e0d805"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3f66014e407a41a487f689582e04c56e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/236 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "  0%|          | 0/60 [00:00<?, ?it/s]"
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        }
      ],
      "source": [
        "base = DATA_DIR / 'mri/'\n",
        "base.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "train_img = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] ==30, p_uimap(process_scan, train_paths))))\n",
        "test_img = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] ==30, p_uimap(process_scan, test_paths))))\n",
        "#train_img = np.concatenate(train_img, axis = 2).T\n",
        "#test_img = np.concatenate(test_img,axis = 2).T\n",
        "np.save(base/'30-xtrain.npy', train_img)\n",
        "np.save(base/'30-xtest.npy', test_img)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eUlt26y5F03W"
      },
      "outputs": [],
      "source": [
        "ytest = np.load(DATA_DIR/'mri'/'subjects_ytest.npy')\n",
        "ytrain = np.load(DATA_DIR/'mri'/'subjects_ytrain.npy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czlss9ObGXGk",
        "outputId": "fc2db89f-d7b5-4c2e-fa5b-400de7cc1577"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(60,)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ytest.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qDtH5uWLJy80"
      },
      "outputs": [],
      "source": [
        "y_train = np.concatenate(list(map(lambda x: [x]*50, ytrain)))\n",
        "y_test = np.concatenate(list(map(lambda x: [x]*50, ytest)))\n",
        "np.save(DATA_DIR/'folds/validation/ytrain.npy', y_train)\n",
        "np.save(DATA_DIR/'folds/validation/ytest.npy', y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uaadTaYNLIG"
      },
      "source": [
        "We can then apply a repeated stratified 10-fold cross validation on the previously created train set by saving each fold to a file. Each fold will then be used as validation set in future steps."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xJOSq_bXGVQ0"
      },
      "outputs": [],
      "source": [
        "# 10 Fold on subjects\n",
        "from sklearn.model_selection import RepeatedStratifiedKFold\n",
        "\n",
        "subjects_xtrain = np.load(DATA_DIR/'mri'/'subjects_xtrain.npy', allow_pickle=True)\n",
        "subjects_ytrain = np.load(DATA_DIR/'mri'/'subjects_ytrain.npy', allow_pickle=True)\n",
        "base = DATA_DIR /'subjects'\n",
        "kFold = RepeatedStratifiedKFold(n_splits=10, n_repeats=10, random_state = RANDOM_SEED)\n",
        "traintest = kFold.split(subjects_xtrain,subjects_ytrain)\n",
        "\n",
        "for k, (train, test) in enumerate(traintest):\n",
        "    print('k =',k+1)\n",
        "    fold_dir = base / f'fold-{k + 1}'\n",
        "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "    np.save(fold_dir / 'xtrain.npy', subjects_xtrain[train])\n",
        "    np.save(fold_dir / 'ytrain.npy', subjects_ytrain[train])\n",
        "    np.save(fold_dir / 'xtest.npy', subjects_xtrain[test])\n",
        "    np.save(fold_dir / 'ytest.npy', subjects_ytrain[test])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ESFeC0lBLKXR"
      },
      "outputs": [],
      "source": [
        "mri = dict((map(lambda s: (re.sub(r'^.*/sub-(.*?)_ses-(d\\d+?)_.*?\\.nii(?:\\.gz)?',r'\\1_MR_\\2',s),s), mri)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l1bnj-0D-zkH"
      },
      "outputs": [],
      "source": [
        "base = DATA_DIR/'subjects'\n",
        "for i in range(1,101):\n",
        "  print(i)\n",
        "  folder = base/f'fold-{i}'\n",
        "  # Load arrays of folder's names\n",
        "  #x_train = np.load(folder/'xtrain.npy', allow_pickle=True)\n",
        "  y_train = np.load(folder/'ytrain.npy', allow_pickle=True)\n",
        "  #x_test = np.load(folder/'xtest.npy', allow_pickle=True)\n",
        "  y_test = np.load(folder/'ytest.npy', allow_pickle=True)\n",
        "  # Map folder name to image path\n",
        "  #train_paths = list(map(lambda x: mri[x] , x_train))\n",
        "  #test_paths = list(map(lambda x: mri[x] , x_test))\n",
        "  #load images from train/test split\n",
        "  #train_img = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] ==30, p_uimap(process_scan, train_paths))))\n",
        "  #test_img = np.array(list(filter(lambda x: x is not None and np.array(x).shape[-1] ==30, p_uimap(process_scan, test_paths))))\n",
        "  # generate reshaped images and save them\n",
        "  folds = DATA_DIR/f'folds/fold-{i}'\n",
        "  #xtrain = np.concatenate(train_img, axis = 2).T\n",
        "  #xtest = np.concatenate(test_img, axis = 2).T\n",
        "  ytrain = np.concatenate(list(map(lambda x: [x]*30, y_train)))\n",
        "  ytest = np.concatenate(list(map(lambda x: [x]*30, y_test)))\n",
        "  #np.save(folds/'xtrain.npy', xtrain)\n",
        "  #np.save(folds/'xtest.npy', xtest)\n",
        "  np.save(folds/'ytrain.npy', ytrain)\n",
        "  np.save(folds/'ytest.npy', ytest)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SUoucvlCv3Oi",
        "outputId": "e5c9e587-622e-4ee4-d537-e95e6c9448a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(11800, 128, 128)\n",
            "(11800,)\n",
            "(3000, 128, 128)\n",
            "(3000,)\n"
          ]
        }
      ],
      "source": [
        "xtrain = train_img\n",
        "xtest = test_img\n",
        "ytrain = y_train\n",
        "ytest = y_test\n",
        "print(xtrain.shape)\n",
        "print(ytrain.shape)\n",
        "print(xtest.shape)\n",
        "print(ytest.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3ZUg0sFT2xiq"
      },
      "outputs": [],
      "source": [
        "y_test = np.load(DATA_DIR/'mri'/'50-ytest.npy')\n",
        "y_train = np.load(DATA_DIR/'mri'/'50-ytrain.npy')\n",
        "x_test = np.load(DATA_DIR/'mri'/'20-xtest.npy')\n",
        "x_train = np.load(DATA_DIR/'mri'/'20-xtrain.npy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u2VIqcvi3tCg",
        "outputId": "94080320-4533-415e-bdff-5e4af2a73b39"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(60,) (236,) (236, 128, 128, 20) (60, 128, 128, 20)\n"
          ]
        }
      ],
      "source": [
        "print(y_test.shape,y_train.shape,x_train.shape,x_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ASY43CmnGJKX",
        "outputId": "5316b2df-b565-4716-92c5-b58bdc3a005c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "k = 1\n",
            "k = 2\n",
            "k = 3\n",
            "k = 4\n",
            "k = 5\n",
            "k = 6\n",
            "k = 7\n",
            "k = 8\n",
            "k = 9\n",
            "k = 10\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "base = DATA_DIR / 'mri/3d-folds/20'\n",
        "kFold = StratifiedKFold(n_splits=10, random_state=RANDOM_SEED, shuffle=True)\n",
        "traintest = kFold.split(x_train,y_train)\n",
        "\n",
        "for k, (train, test) in enumerate(traintest):\n",
        "    print('k =',k+1)\n",
        "    fold_dir = base / f'fold-{k + 1}'\n",
        "    fold_dir.mkdir(parents=True, exist_ok=True)\n",
        "\n",
        "    np.save(fold_dir / 'xtrain.npy', x_train[train])\n",
        "    np.save(fold_dir / 'ytrain.npy', y_train[train])\n",
        "    np.save(fold_dir / 'xtest.npy', x_train[test])\n",
        "    np.save(fold_dir / 'ytest.npy', y_train[test])"
      ]
    }
  ],
  "metadata": {
    "authors": [
      {
        "name": "Andrea Esposito"
      },
      {
        "name": "Graziano Montanaro"
      }
    ],
    "colab": {
      "provenance": []
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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    "subtitle": "Creating the Dataset",
    "title": "Automatic Alzheimer's Disease Diagnosis Using PET Scans: Comparing Different Approaches",
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