Weak-Mamba-UNet is a paradigm-shifting approach to medical image segmentation. Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation takes weakly-supervised learning to new heights by integrating three symmetrical encoder-decoder networks: CNN-based UNet, Swin Transformer-based SwinUNet, and VMamba-based Mamba-UNet. This collaborative framework harnesses pseudo labels for iterative learning across networks, with notable results on MRI cardiac segmentation datasets.

• Innovative use of weak supervision for medical imaging.
• Pseudo labels facilitate cross-network learning.
• Surpasses UNet or SwinUNet performance.
• Potential for scenarios with imprecise annotations.
• Publicly available source code.

The significance of Weak-Mamba-UNet lies in its ability to improve image segmentation under limited annotations, suggesting broader applications in medical imaging and beyond. Its open-source nature also encourages community-driven improvements and variations.