SCKansformer

We propose a novel fine-grained classification model, SCKansformer, for bone marrow blood cells. The SCKansformer model primarily comprises three parts: Kansformer Encoder, SCConv Encoder and Global-Local Attention Encoder. The overall architecture of our proposed SCKansformer model:

1. Environment

• Please clone this repository and navigate to it in your terminal.
• Then prepare an environment with python=3.8, and then use the command pip install -r requirements.txt for the dependencies.

2. Train/Test

• Put the BMCD-FGCD dataset(PBC/ALL-IDB dataset) into data/BM_data(PBC_data/ALL_data), then split folders by category and modify the class_indices.json file.
• Run train_SCkansformer_cell.py to Train/Test in data/BM_data.
• The batch size we used is 40 for V100. If you do not have enough GPU memory, the bacth size can be reduced to 30 for GeForce RTX 4090 or 6 to save memory.

3. BMCD-FGCD dataset

In collaboration with the Department of Hematology at Zhejiang Hospital in Hangzhou, Zhejiang Province, our team has established the Bone Marrow Cell Dataset for Fine-Grained Classification (BMCD-FGCD), containing over 10,000 data points across nearly forty classifications. We have made our private BMCD-FGCD dataset available to other researchers, contributing to the field's advancement. If you want to use our private dataset, please cite our article.

Download link is available at https://drive.google.com/file/d/1hOmQ9s8eE__nqIe3lpwGYoydR4_UNRrU/view?usp=drive_link.

Details of our BMCD-FGCD dataset:

4. Establishment and Usage of our BMCD-FGCD dataset

Workflow of the establishment of our BMCD-FGCD dataset:

Below, we delineate the specific utility of our BMCD-FGCD dataset in various application contexts:

• Training of Deep Learning Models and Automatic Blood Cell Identification.
• Integrated Diagnosis with Clinical Data.
• Identification of Rare and Atypical Blood Cells.

5. Citation

@article{chen2024sckansformer,
  title={Sckansformer: Fine-grained classification of bone marrow cells via kansformer backbone and hierarchical attention mechanisms},
  author={Chen, Yifei and Zhu, Zhu and Zhu, Shenghao and Qiu, Linwei and Zou, Binfeng and Jia, Fan and Zhu, Yunpeng and Zhang, Chenyan and Fang, Zhaojie and Qin, Feiwei and others},
  journal={IEEE Journal of Biomedical and Health Informatics},
  year={2024},
  publisher={IEEE}
}