cdc

This is a PyTorch implementation of the paper "Weakly supervised histopathological image representation learning based on contrastive dynamic clustering".

@inproceedings{li2022weakly,
  author    = {Jun Li, Zhiguo Jiang, Yushan Zheng, Haopeng Zhang, Jun Shi, Dingyi Hu,
               Wei Luo, Zhongmin Jiang, and Chenghai Xue},
  title     = {Weakly Supervised Histopathological Image Representation Learning based on Contrastive Dynamic Clustering},
  booktitle = {SPEI Medical Imaging 2022},
  year      = {2022},
}

Our code is modified from repository simsiam.

Data Preparation

This code use "train.txt" to store the path and pseudo-label of images. An example of "train.txt" file is described as follows:

<path>                         <pseudo-label>
[path to slide1]/0000_0000.jpg 0
[path to slide1]/0000_0001.jpg 0
...
[path to slide2]/0000_0000.jpg 1
...

Note: we assign the pseudo-label for the patches from a WSI as the same of the WSI.

Training

Firstly, training the BYOL for initializing the patch features.

python main_byol.py \
  -a resnet50 \
  --dist-url 'tcp://192.168.0.1:10002' --multiprocessing-distributed --world-size 1 --rank 0\
  --fix-pred-lr \
  [your train.txt file folders]

Secondly, fine-tuning the encoder with our cdc module.

python main_cdc.py \
  -a resnet50 \
  --dist-url 'tcp://192.168.0.1:10002' --multiprocessing-distributed --world-size 1 --rank 0\
  --pretrained './checkpoint-byol/checkpoint_0099.pth.tar' \
  [your train.txt file folders]