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Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

Introduction

This is an official release of the paper Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation.

It is accepted by AAAI-2022 Oral and has been awarded an AAAI student scholarship.

Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation, <br/> Jiacheng Wang, Xiaomeng Li, Yiming Han, Jing Qin, Liansheng Wang, Zhou Qichao<br/> In: Association for the Advancement of Artificial Intelligence (AAAI), 2022 <br/> [arXiv][Bibetex]

TODO List

1. Complete the resources ...

2. Evaluate the effectiveness on more vision tasks ...

Code List

• Comparison Methods, Here
• Network
• Pre-processing
• Training Codes

Usage

1. First, you can download the dataset at PDDCA. To preprocess the dataset and save as ".png", run:

   $ python utils/prepare_data.py
   

   Note that some cases lack the complete annotation, so that we can obtain 32 cases with full annotation in the end.

2. To create the region set, alternatively run:

   $ python utils/prepare_segs.py --dataset pddca --filter_method all --seg_method fb --min_size 400
   $ python utils/prepare_segs.py --dataset pddca --filter_method all --seg_method slic --n_segments 32
   $ python utils/prepare_segs.py --dataset pddca --filter_method all --seg_method slice --n_segments 32
   

Citation

If you find SepaReg useful in your research, please consider citing:

@inproceedings{wang2022separated,
  title={Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation},
  author={Wang, Jiacheng and Li, Xiaomeng and Han, Yiming and Qin, Jing and Wang, Liansheng and Qichao, Zhou},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={36},
  number={3},
  pages={2459--2467},
  year={2022}
}