AC-MAMBASEG：一种基于自适应卷积与Mamba的增强皮肤病变分割架构
AC-MAMBASEG: An adaptive convolution and Mamba-based architecture for enhanced skin lesion segmentation
- Hanoi University of Science and Technology（河内科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出AC-MambaSeg皮肤病变分割模型，通过混合CNN-Mamba骨干并结合CBAM、注意力门和选择性核瓶颈，在ISIC-2018和PH2数据集上验证了其提升分割精度和辅助皮肤病早期诊断的有效性。
AI中文摘要：
皮肤病变分割是皮肤病计算机辅助诊断系统中的关键任务。从医学图像中准确分割皮肤病变对于早期检测、诊断和治疗规划至关重要。本文提出了一种新的皮肤病变分割模型AC-MambaSeg，该模型具有混合CNN-Mamba骨干，并集成了卷积块注意力模块（CBAM）、注意力门和选择性核瓶颈等先进组件。AC-MambaSeg利用Vision Mamba框架进行高效特征提取，同时CBAM和选择性核瓶颈增强了其聚焦信息区域和抑制背景噪声的能力。我们在包括ISIC-2018和PH2在内的多种皮肤病变图像数据集上评估了AC-MambaSeg的性能，并将其与现有分割方法进行比较。我们的模型在改进计算机辅助诊断系统、促进皮肤病早期检测和治疗方面展现出良好潜力。源代码将在以下地址公开：https://github.com/vietthanh2710/AC-MambaSeg。
英文摘要：
Skin lesion segmentation is a critical task in computer-aided diagnosis systems for dermatological diseases. Accurate segmentation of skin lesions from medical images is essential for early detection, diagnosis, and treatment planning. In this paper, we propose a new model for skin lesion segmentation namely AC-MambaSeg, an enhanced model that has the hybrid CNN-Mamba backbone, and integrates advanced components such as Convolutional Block Attention Module (CBAM), Attention Gate, and Selective Kernel Bottleneck. AC-MambaSeg leverages the Vision Mamba framework for efficient feature extraction, while CBAM and Selective Kernel Bottleneck enhance its ability to focus on informative regions and suppress background noise. We evaluate the performance of AC-MambaSeg on diverse datasets of skin lesion images including ISIC-2018 and PH2; then compare it against existing segmentation methods. Our model shows promising potential for improving computer-aided diagnosis systems and facilitating early detection and treatment of dermatological diseases. Our source code will be made available at: https://github.com/vietthanh2710/AC-MambaSeg.
