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

arXiv:2403.17701v1 Announce Type: cross
Abstract: 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 …

abstract arxiv challenges cnns convolutional neural networks cs.cv cs.lg diagnosis domain eess.iv image mamba medical networks neural networks segmentation ssm transformer transformer models treatment type unet vital