Domain Generalization on Medical Imaging Classification Using Episodic Training with Task Augmentation (CBM 2021) (Link)

A Pytorch Implementation of ''Domain Generalization on Medical Imaging Classification Using Episodic Training with Task Augmentation'', which is accepted by the jounal of Computers in Biology and Medicine.

Requirements

• Python == 3.7.4
• Tensorflow == 1.14.0
• CUDA 8.0

Epithelium-stroma classification

Dataset

You can download the annotated pathological datasets of VGH, NKI, IHC and NCH from here.

Train

python main_mame.py

Test

python test_mame.py

Liver segmentation

Train

python main_seg_mame.py

Test

python test_seg_mame.py

Results on epithelium-stroma classification

Source	Target	MLDG	Epi-FCR	MetaReg	JiGen	MASF	Ours
NKI,IHC,NCH	VGH	91.13	91.49	91.74	92.05	92.43	93.51

Results on liver segmentation

Source	Target	MLDG	Epi-FCR	MetaReg	JiGen	MASF	Ours
BTCV,CHAOS,LITS	IRCAD	89.17	89.26	89.17	91.44	90.89	92.14

Citation

If you find this repository useful, please cite our paper:

@article{li2022domain,
  title={Domain generalization on medical imaging classification using episodic training with task augmentation},
  author={Li, Chenxin and Lin, Xin and Mao, Yijin and Lin, Wei and Qi, Qi and Ding, Xinghao and Huang, Yue and Liang, Dong and Yu, Yizhou},
  journal={Computers in Biology and Medicine},
  volume={141},
  pages={105144},
  year={2022},
  publisher={Elsevier}
}