Skip to content

[AAAI 2022 Oral] Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

Notifications You must be signed in to change notification settings

jcwang123/Separate_CL

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

26 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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,
Jiacheng Wang, Xiaomeng Li, Yiming Han, Jing Qin, Liansheng Wang, Zhou Qichao
In: Association for the Advancement of Artificial Intelligence (AAAI), 2022
[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}
}

About

[AAAI 2022 Oral] Separate Contrastive Learning for Organs-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages