Replication Data for: GraphScrib: Integrating Graph Neural Networks with CNN-Transformer for Medical Image Segmentation

The research introduces a novel weakly-supervised semantic segmentation (WSSS) framework that unifies Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Graph Neural Networks (GNNs) to address the partial activation anomaly in medical imaging.

Contents of this repository: Scribble Annotations: The generated sparse scribble masks used as weak supervision signals. Data Splits: The exact Train/Validation/Test patient splits used for the ACDC and MSCMRseg datasets to ensure fair comparison and reproducibility. Pre-trained Weights: The final model weights for GraphScrib achieving the reported Dice scores.