DFQ: Learning Generalized Medical Image Segmentation from Decoupled Feature Queries

This is the official implementation of our work entitled DFQ: Learning Generalized Medical Image Segmentation from Decoupled Feature Queries, which has been accepted by AAAI2024.

An example of training and inference is given below.

Environment Configuration

The basic enviroment dependencies include:

    pip install torchvision==0.8.2
    pip install timm==0.3.2
    pip install mmcv-full==1.2.7
    pip install opencv-python==4.5.1.48

For other minor packages, please refer to the requirements.txt file in this project.

You can directly set up all the enviroment dependencies by

pip install -r requirements.txt

Training on Source Domain

An example of training on DD Fundus benchmark with domain-0 as unseen target domain is given below.

python -W ignore train_feed.py --data_root D:/Med/dataset --dataset fundus --domain_idxs 1,2,3 --test_domain_idx 0 --is_out_domain --consistency --consistency_type kd --encoder b3 --save_path outdir/fundus/target0_pretrain_0.99_b3_feed_iw

Inference on Unseen Target Domains

An example of inference on a pre-trained model is given below.

python -W ignore test_fundus_slice_feed.py --model_file outdir/fundus/target0_pretrain_0.99_b3_feed_iw/model_xx.xx.pth --dataset fundus --data_dir D:/Med/dataset --datasetTest 0 --encoder b3 --test_prediction_save_path results/fundus/target0_pretrain_0.99_b3_feed_iw_xx.xx --save_result

By using this CMD, not only the numerical results but also the visual prediction can be outputted. Here model_xx.xx.pth refers to the name of a pre-trained model, where x refers to a number value.

Citation

If you find our work useful, please cite as

@inproceedings{bi2024learning,
  title={Learning Generalized Medical Image Segmentation from Decoupled Feature Queries},
  author={Bi, Qi and Yi, Jingjun and Zheng, Hao and Ji, Wei and Huang, Yawen and Li, Yuexiang and Zheng, Yefeng},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={2},
  pages={810--818},
  year={2024}
}

Acknowledgement

The development of Decoupled Feature Queries (DFQ) largely relies on two prior projects:

(1) The code of dataloader is based on RAM-DSIR published in ECCV2022, with the code link [https://github.com/zzzqzhou/RAM-DSIR].

(2) The code of feature as query is highly based on FeedFormer published in AAAI2023, with the code link [https://github.com/jhshim1995/FeedFormer].

BiQiWHU / DFQ