HCMA-UNet: A Hybrid CNN-Mamba UNet with Inter-Slice Self-Attention for Efficient Breast Cancer Segmentation

• URL: http://arxiv.org/abs/2501.00751v1
• Date: Wed, 01 Jan 2025 06:42:57 GMT
• Title: HCMA-UNet: A Hybrid CNN-Mamba UNet with Inter-Slice Self-Attention for Efficient Breast Cancer Segmentation
• Authors: Haoxuan Li, Wei song, Peiwu Qin, Xi Yuan, Zhenglin Chen,
• Abstract summary: This study proposes a novel hybrid segmentation network, HCMA-UNet, for lesion segmentation of breast cancer.<n>Our network consists of a lightweight CNN backbone and a Multi-view Inter-Slice Self-Attention Mamba (MISM) module.<n>Our lightweight model achieves superior performance with 2.87M parameters and 126.44 GFLOPs.
• Score: 7.807738181550226
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Breast cancer lesion segmentation in DCE-MRI remains challenging due to heterogeneous tumor morphology and indistinct boundaries. To address these challenges, this study proposes a novel hybrid segmentation network, HCMA-UNet, for lesion segmentation of breast cancer. Our network consists of a lightweight CNN backbone and a Multi-view Inter-Slice Self-Attention Mamba (MISM) module. The MISM module integrates Visual State Space Block (VSSB) and Inter-Slice Self-Attention (ISSA) mechanism, effectively reducing parameters through Asymmetric Split Channel (ASC) strategy to achieve efficient tri-directional feature extraction. Our lightweight model achieves superior performance with 2.87M parameters and 126.44 GFLOPs. A Feature-guided Region-aware loss function (FRLoss) is proposed to enhance segmentation accuracy. Extensive experiments on one private and two public DCE-MRI breast cancer datasets demonstrate that our approach achieves state-of-the-art performance while maintaining computational efficiency. FRLoss also exhibits good cross-architecture generalization capabilities. The source code and dataset is available on this link.

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