PointTransformerFL

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PointTransformerFL

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A Point Transformer with Federated Learning for HER2 Status Prediction

PointTransformerFL 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# 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](https://boyden.github.io), Zhenyu Liu, Lizhi Shao, Bensheng Qiu, Hong Bu, Jie Tian

### [[arXiv]](https://arxiv.org/abs/2312.06454)

### 🔥 News
- *2023.12*: 🎉 PointTransformerFL is accepted by AAAI 2024!

## PointTransformerDDA+

The overall framework of PointTransformerDDA+.

![point_transformer](https://raw.githubusercontent.com/boyden/PointTransformerFL/main/docs/point_transformer.png)

## Environment Settings

```bash
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
For feature extractor, we use the model trained with nuclei instance segmentation tasks. Please refer the repo [NuHTC](https://github.com/boyden/NuHTC).

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

```bash
# 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

```bash
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.

```bash
# 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](https://github.com/qq456cvb/Point-Transformers)<br>
[mahmoodlab/HistoFL](https://github.com/mahmoodlab/HistoFL)

## Cite
If it is helpful for your work, please cite this paper:
```tex
@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}
}
```

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