📌 This is an official PyTorch implementation of [TNNLS 2023] - Decoupled Unbiased Teacher for Source-Free Domain Adaptive Medical Object Detection.
Decoupled Unbiased Teacher for Source-Free Domain Adaptive Medical Object Detection
Xinyu Liu, Wuyang Li, Yixuan Yuan
The Chinese Univerisity of Hong Kong, City University of Hong Kong
Please check INSTALL.md for installation instructions.
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Download datasets from the following sources:
Source Domain:
Target Domain:
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Change the masks to coco style. Please refer to this link or write a script.
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Download datasets and corresponding coco format annotations in the following links:
Source Domain:
CVC-ClinicDB: gDrive
Target Domain:
Abnormal Symptoms: gDrive
Change the dataset dir to your downloaded data path in here.
Download the Source-only model from gDrive.
# Train the DUT on abnormal symptoms
python tools/train_net_mcd.py --config ./configs/sf/dut_hcmus.yaml OUTPUT_DIR outputs/dut_hcmus
# Test the trained model
python tools/train_net_mcd.py --config-file configs/sf/dut_hcmus.yaml SOLVER.TEST_ONLY True MODEL.WEIGHT $YOUR .pth WEIGHT$
If you find this work or codebase is useful for your research, please give it a star and citation. We sincerely appreciate for your acknowledgments.
@article{liu2023decoupled,
title={Decoupled Unbiased Teacher for Source-Free Domain Adaptive Medical Object Detection},
author={Liu, Xinyu and Li, Wuyang and Yuan, Yixuan},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2023},
publisher={IEEE}
}
A relevant research can be found at SMPT, which was our earlier work on source-free polyp detection.
@article{liu2022source,
title={A source-free domain adaptive polyp detection framework with style diversification flow},
author={Liu, Xinyu and Yuan, Yixuan},
journal={IEEE Transactions on Medical Imaging},
volume={41},
number={7},
pages={1897--1908},
year={2022},
publisher={IEEE}
}
The code is based on FCOS. Thanks for the excellent framework. For enquiries please contact xinyuliu@link.cuhk.edu.hk.