Abstract
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: this https URL.
Abstract (translated)
皮肤病变分割是计算机辅助诊断系统皮肤疾病诊断中的一项关键任务。准确从医学图像中分割皮肤病变是早期诊断、诊断和治疗规划的必要条件。在本文中，我们提出了一个名为AC-MambaSeg的新模型用于皮肤病变分割，这是一种增强模型，具有混合CNN-Mamba骨干网络和高级组件，如卷积块注意模块（CBAM）、注意门和选择性内核瓶颈。AC-MambaSeg利用Vision Mamba框架进行高效的特征提取，而CBAM和选择性内核瓶颈则增强了其关注有信息区域并抑制背景噪声的能力。我们在包括ISIC-2018和PH2等多样数据集的皮肤病变图像上评估AC-MambaSeg的性能，然后与现有分割方法进行比较。我们的模型在改善计算机辅助诊断系统和促进早期诊断和治疗皮肤疾病方面具有令人鼓舞的潜力。我们的源代码将在此处公布：https://this URL。
URL
https://arxiv.org/abs/2405.03011
