Weak-Mamba-UNet: Visual Mamba Makes CNN and ViT Work Better for Scribble-based Medical Image Segmentation

arXiv:2402.10887v1 Announce Type: cross
Abstract: 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 …

abstract annotation arxiv capabilities cnn convolutional neural network costs cs.cv deep learning deep learning techniques eess.iv framework image mamba medical network neural network paper performance segmentation supervised learning transformer type unet vision visual vit weakly-supervised work wsl