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

Related tags

Deep Learningsegmentationcontrastive-learningaaai2022
Overview

A paper

Introduction

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

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, Qichao Zhou
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:

Owner
Jiacheng Wang
Medical Imaging Processing
Jiacheng Wang
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