/Cyclic-learning

This repo provides the official code for : Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation. This is the experiment part 2.

Primary LanguageJupyter NotebookMIT LicenseMIT

Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation (experiment part 2)

Official implementation of Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation. The original paper link is here: TMI link. This project provide code for experiments based on hovernet. Link to Experiment part 1.

Installation

  • Our project is developed on Hovernet. We modify some config for our specific task.

  • Create an environment meets the requirements as listed in requirements.txt

Data Preparation

  • Download the Monusac dataset (pwd：mseg) and cropped Monusac dataset (pwd : mseg), and put it in the Mask_RCNN/datasets/ directory.

  • Download the ccrcc dataset (pwd：mseg), and put it in the Mask_RCNN/datasets/ directory.

  • Download the consep dataset (pwd：mseg), and put it in the Mask_RCNN/datasets/ directory.

  • Download the positive-and-negative nucleus image classification dataset (pwd : mseg) which is obtained by cropping out tile images from TCGA WSI(whole slide image). Datasets are organized in the following way:

datasets/
    MyNP/
        negative/
        positive/
    MoNuSACGT/
    MoNuSACCROP/
        stage1_train/
        images/
        masks/
    ccrcccrop/    
        Test/
        Train/
        Valid/
    consepcrop/
        Test/
        Train/
        Valid/

Training

Before training, please download pretrain weights of big nature image datasets, for which we use COCO pretrain weights. Remember to change the path in the code. Training Cyclic Learning on MoNusac dataset:

python cyclic.py

For ccrcc and consep datasets, please refer to current version and change some paths.

Citing Cyclic Learning

If you use Cyclic Learning in your work or wish to refer to the results published in this repo, please cite our paper:

@ARTICLE{zhou2023cyclic,
  author={Zhou, Yang and Wu, Yongjian and Wang, Zihua and Wei, Bingzheng and Lai, Maode and Shou, Jianzhong and Fan, Yubo and Xu, Yan},
  journal={IEEE Transactions on Medical Imaging}, 
  title={Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation}, 
  year={2023},
  doi={10.1109/TMI.2023.3275609}}