Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for
  Scribble-based Medical Image Segmentation
Ziyang Wang Chao Ma
Medical image segmentation is increasingly reliant on deep learning
techniques, yet the promising performance often come with high annotation
costs. This paper introduces Weak-Mamba-UNet, an innovative weakly-supervised
learning (WSL) framework that leverages the capabilities of Convolutional
Neural Network (CNN), Vision Transformer (ViT), and the cutting-edge Visual
Mamba (VMamba) architecture for medical image segmentation, especially when
dealing with scribble-based annotations. The proposed WSL strategy incorporates
three distinct architecture but same symmetrical encoder-decoder networks: a
CNN-based UNet for detailed local feature extraction, a Swin Transformer-based
SwinUNet for comprehensive global context understanding, and a VMamba-based
Mamba-UNet for efficient long-range dependency modeling. The key concept of
this framework is a collaborative and cross-supervisory mechanism that employs
pseudo labels to facilitate iterative learning and refinement across the
networks. The effectiveness of Weak-Mamba-UNet is validated on a publicly
available MRI cardiac segmentation dataset with processed scribble annotations,
where it surpasses the performance of a similar WSL framework utilizing only
UNet or SwinUNet. This highlights its potential in scenarios with sparse or
imprecise annotations. The source code is made publicly accessible.