Cluster-Re-Supervision：Bridging the Gap Between Image-Level and Pixel-Wise Labels for Weakly Supervised Medical Image Segmentation

Roughly to specifically: Mining specific constraints via unsupervised learning for weakly supervised medical image segmentation

This code is a simple implemention example of on the BraTS2019 dataset.

1. Transform your dataset from 3D nii scans to 2D h5 slices with nii_h5.py, or you can recode the dataset.py
2. Run the group.py to obtain the ratio between the positive and negtive samples.
3. Train the model：class_km.py

It should be noted that this framework just generate the class activation maps, and you can use the CAMs as pseudo labels to train a segmentation network e.g. U-Net, further improve the segmentation performance.