Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation

Understand

Image segmentation holds a vital position in the realms of diagnosis and treatment within the medical domain.

• Traditional convolutional neural networks (CNNs) and Transformer models have made significant advancements in this realm, but they still encounter challenges because of limited receptive field or high computing complexity.
• Recently, State Space Models (SSMs), particularly Mamba and its variants, have demonstrated notable performance in the field of vision.
• However, their feature extraction methods may not be sufficiently effective and retain some redundant structures, leaving room for parameter reduction.