Awesome
DoFE: Domain-oriented Feature Embeddingfor Generalizable Fundus Image Segmentationon Unseen Datasets
by Shujun Wang, Lequan Yu, Kang Li, Yang Xin, Chi-Wing Fu, and Pheng-Ann Heng.
Introduction
This repository is for our TMI2020 paper 'DoFE: Domain-oriented Feature Embeddingfor Generalizable Fundus Image Segmentationon Unseen Datasets'. Schematic diagram of our proposedDoFEframework to utilize multi-source domain datasets {D1,...,DK} forgeneralizable segmentation on the unseen dataset D_{K+1}. We adopt the Domain Knowledge PoolMpoolto learn and memorizethe multi-source domain prior knowledge. Importantly, our framework dynamically enriches the image semantic featurehswith domain-oriented aggregated featureˆhaggextracted fromMpoolto improve the expressiveness of the semantic feature.
Requirements
- python 3.6.8
conda create -n DOFE python=3.6.8
-
PyTorch 1.5.0
conda activate DOFE conda install pytorch==1.5.0 torchvision cudatoolkit=9.2 -c pytorch pip install tensorboardX==2.0 pip install opencv-python pip install pyyaml pip install MedPy conda install -c anaconda scikit-image
Usage
-
Clone the repository and download the dataset into your own folder and change
--data-dir
correspondingly. -
Train the model.
python train.py -g 0 --datasetTrain 1 2 3 --datasetTest 4 --batch-size 16 --resume ./pretrained-weight/test4-epoch40.pth.tar # You need to pretrain a model
-
Test the model.
python test.py --model-file ./logs/test4/lam0.9/20201120_215812.079473/checkpoint_80.pth.tar --datasetTest 4 -g 0
Citation
If DoFE is useful for your research, please consider citing:
@article{wang2020dofe,
title={DoFE: Domain-oriented Feature Embedding for Generalizable Fundus Image Segmentation on Unseen Datasets},
author={Wang, Shujun and Yu, Lequan and Li, Kang and Yang, Xin and Fu, Chi-Wing and Heng, Pheng-Ann},
journal={IEEE Transactions on Medical Imaging},
year={2020},
publisher={IEEE}
}