Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation
CoRR(2024)
摘要
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.
Motivated by previous spatial and channel attention methods, we propose Triplet
Mamba-UNet. The method leverages residual VSS Blocks to extract intensive
contextual features, while Triplet SSM is employed to fuse features across
spatial and channel dimensions. We conducted experiments on ISIC17, ISIC18,
CVC-300, CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB, and Kvasir-Instrument datasets,
demonstrating the superior segmentation performance of our proposed TM-UNet.
Additionally, compared to the previous VM-UNet, our model achieves a one-third
reduction in parameters.
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