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imos's Introduction

iMOS

Implementation of "A Foundation Model for General Moving Object Segmentation in Medical Images" (accepted by IEEE ISBI 2024 (Oral))

arXiv

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Dataset

Source Modality Link
AMOS2022 CT / MRI link
CAMUS Ultrasound link
CholecSeg8k Endoscopy link
EPFL Electron Microscopy link

Run

python -m torch.distributed.launch --master_port 25763 --nproc_per_node=2 train.py --exp_id retrain_stage3_only --stage 3 --load_network XMem.pth

Pretrained XMem weight can be found here.

Acknowledgments

Our code is based on XMem. We appreciate the authors for their great works.

Citation

@misc{yan2023foundation,
      title={A Foundation Model for General Moving Object Segmentation in Medical Images}, 
      author={Zhongnuo Yan and Tong Han and Yuhao Huang and Lian Liu and Han Zhou and Jiongquan Chen and Wenlong Shi and Yan Cao and Xin Yang and Dong Ni},
      year={2023},
      eprint={2309.17264},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

imos's People

Contributors

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Watchers

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Forkers

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imos's Issues

Inquiry Regarding Model Checkpoint and Config Files

Hi,
thank you for publishing your interesting work.I was wondering if you have plans to publish the checkpoint of your trained model on medical datasets, along with the corresponding config file used for training. Additionally, if you could provide guidance on how one could effectively test this model on custom datasets, it would be immensely helpful.

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