轻量级模型MambaU-Lite提升皮肤病变分割精度
MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation
- 融合Mamba与CNN，设计通道-空间注意力块
- 仅40万参数，计算量超1G Flops，适合医疗设备部署
- 在ISIC2018和PH2数据集上表现优异，适合临床辅助诊断
早期发现皮肤异常对皮肤癌的诊断与治疗至关重要。基于AI的皮肤病变区域分割已广泛应用，有助于辅助诊断。然而，由于需要高分辨率图像且病灶边界模糊，实现高性能仍具挑战性。同时，医疗设备要求分割模型具备小内存占用和低计算成本。为此，我们提出一种新型轻量级模型MambaU-Lite，结合Mamba与CNN架构优势，参数量仅40余万，计算量超过1G Flops。为增强全局上下文与局部特征提取，我们设计P-Mamba模块，融合VSS块与多层池化结构，有效学习多尺度特征，提升分割性能。在ISIC2018与PH2两个皮肤数据集上评估，结果表现良好。源代码将公开于：https://github.com/nqnguyen812/MambaU-Lite。
原文摘要 · Abstract (English)
Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. However, achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundaries of individual lesions. At the same time, medical devices require segmentation models to have a small memory foot-print and low computational cost. Based on these requirements, we introduce a novel lightweight model called MambaU-Lite, which combines the strengths of Mamba and CNN architectures, featuring just over 400K parameters and a computational cost of more than 1G flops. To enhance both global context and local feature extraction, we propose the P-Mamba block, a novel component that incorporates VSS blocks along-side multiple pooling layers, enabling the model to effectively learn multiscale features and enhance segmentation performance. We evaluate the model's performance on two skin datasets, ISIC2018 and PH2, yielding promising results. Our source code will be made publicly available at: https://github.com/nqnguyen812/MambaU-Lite.
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