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[BMVC'23 Oral] Offical repository of "Rethinking Transfer Learning for Medical Image Classification"

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This is the offical implemention of paper Rethinking Transfer Learning for Medical Image Classification [BMVC'23 oral]

Overview example of TTL on resent50

Usage

Setup

pip

Requires python>=3.10+

See the requirements.txt for environment configuration

pip install -r requirements.txt

Dataset

BIMCV

  • Please download our pre-processed datasets TBA, put under data/ directory and perform following commands:
    cd ./data
    unzip digit_dataset.zip

HAM10000

  • Please download the dataset here, put under data/HAM10000/

PENet Dataset

  • Please download the dataset here, put under data/PENet/ directory and perform following commands:

Train

2D experiment (BIMCV & HAM1000)

block-wise TTL

Please using following commands to train a model with federated learning strategy.

  • --model specify model archicture: resnet50 | densenet201
  • --pretrained specify source domain: imagenet | chexpert
  • --dataset specify target dataset: BIMCV | HAM10000
  • --trunc specify truncation point: {-1, 1, 2, 3}
python main.py --model resnet50 --bs 64 --data_parallel --num_workers 12 --max_epoch 200 --pretrained imagenet --dataset BIMCV --trunc -1 --exp 1 --sub 100

layer-wise TTL

--trunc specify truncation point: {-1, 1, 2, ..., 16}

python main.py --model layerttl_resnet50 --bs 64 --data_parallel --num_workers 12 --max_epoch 200 --pretrained imagenet --dataset BIMCV --trunc -1 --exp 1 --sub 100

Test

block-wise TTL

python main.py --model resnet50 --bs 64 --data_parallel --num_workers 12 --max_epoch 200 --pretrained imagenet --dataset BIMCV --trunc -1 --exp 1 --sub 100

layer-wise TTL

python main.py --model layerttl_resnet50 --bs 64 --data_parallel --num_workers 12 --max_epoch 200 --pretrained imagenet --dataset BIMCV --trunc -1 --exp 1 --sub 100

If you use this code or dataset in you research, please consider citing our paper with the following Bibtex code:

@article{peng2022rethinking,
  title={Rethinking Transfer Learning for Medical Image Classification},
  author={Peng, Le and Liang, Hengyue and Luo, Gaoxiang and Li, Taihui and Sun, Ju},
  journal={medRxiv},
  pages={2022--11},
  year={2022},
  publisher={Cold Spring Harbor Laboratory Press}
}

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