PointTransformerFL

Official PyTorch implementation for the following paper:

Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images

Bao Li, Zhenyu Liu, Lizhi Shao, Bensheng Qiu, Hong Bu, Jie Tian

[arXiv]

🔥 News

• 2023.12: 🎉 PointTransformerFL is accepted by AAAI 2024!

PointTransformerDDA+

The overall framework of PointTransformerDDA+.

Environment Settings

conda create --name point python=3.8
conda activate point
# pytorch 1.12.0 with cuda 11.3
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
# compared graph model are implemented by DGL
conda install -c dglteam/label/cu113 dgl=1.0.2
pip install -r requirements.txt

Run Experiments

Demo for debug the model. We put some point data from public HEROHE dataset in the HER2 directory

# Training using HEROHE and with external dataset HER2C
python main_her2_FL.py --max_epochs 200 --batch_size 4 --fl_avg FedAvg --exp_code fed_avg_demo --aux 1.0 --csv_path dataset_csv/HEROHE_HER2.csv --data_dir HER2 --ind_name her2c

We recommend to use wandb to visualize the model's training progress. To use wandb, please add the argument --wandb in the command line

python main_her2_FL.py --max_epochs 200 --batch_size 32 --fl_avg FedAvg --exp_code fed_avg_demo --aux 1.0 --fast_sim --csv_path data_csv/HEROHE_HER2.csv --ind_name her2c --wandb

Experiments runed in our study, need the whole data.

# Centralized Training
python main_her2_FL.py --exp_code central_baseline --no_fl
# Base PointTransformer
python main_her2_FL.py --fl_avg FedAvg --exp_code PointTransformer
# PointTransformer with FCS
python main_her2_FL.py --fl_avg FedAvg --exp_code PointTransformer+ --fast_sim
# PointTransformer with DDA
python main_her2_FL.py --fl_avg FedAvg --exp_code PointTransformerDDA --aux 1.0
# PointTransformer with FCS and DDA
python main_her2_FL.py --fl_avg FedAvg --exp_code PointTransformerDDA+ --aux 1.0 --fast_sim

Reference By

qq456cvb/Point-Transformers
mahmoodlab/HistoFL

Cite

If it is helpful for your work, please cite this paper:

@inproceedings{li2024point,
  title={Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images},
  author={Li, Bao and Liu, Zhenyu and Shao, Lizhi and Qiu, Bensheng and Bu, Hong and Tian, Jie},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={4},
  pages={3000--3008},
  year={2024}
}