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[ECCV2024] GALoss

This repository is the official implementation of Gradient-Aware for Class-Imbalanced Semi-supervised Medical Image Segmentation, ECCV2024.

Requirements:

Datasets

Dataset I Synapse. Following DHC, 20 samples were split for training, 4 samples for validation, and 6 samples for testing. We use the processed data by MagicNet.

Dataset II AMOS. The processed dataset can be downloaded via this link. Download and place the datasets in ./data/

Dataset III ACDC. We use the code and preprocessed data by SSLMIS.

Running

CUDA_VISIBLE_DEVICES=0 python train_Synapse_CPS.py --seed 1337 --labelnum 4

Reference

Citations

@inproceedings{qi2024gradient,
  title={Gradient-Aware for Class-Imbalanced Semi-supervised Medical Image Segmentation},
  author={Qi, Wenbo and Wu, Jiafei and Chan, Shing Chow},
  booktitle={European Conference on Computer Vision},
  year={2024},
  organization={Springer}
}