Skin cancer is a significant public health problem, and computer-aided diagnosis can help to prevent and treat it. A crucial step for computer-aided diagnosis is accurately segmenting skin lesions in images, which allows for lesion detection, classification, and analysis. However, this task is challenging due to the diverse characteristics of lesions, such as appearance, shape, size, color, texture, and location, as well as image quality issues like noise, artifacts, and occlusions. Deep learning models have recently been applied to skin lesion segmentation, but they have high parameter counts and computational demands, making them unsuitable for mobile health applications. To address this challenge, we propose UCM-Net, a novel, efficient, and lightweight solution that integrates Multi-Layer Perceptions (MLP) and Convolutional Neural Networks (CNN). Unlike conventional UNet architectures, our UCMNet-Block reduces parameter overhead and enhances UCM-Net's learning capabilities, leading to robust segmentation performance. We validate UCM-Net's competitiveness through extensive experiments on isic2017 and isic2018 datasets. Remarkably, UCM-Net has less than 50KB parameters and less than 0.05 Giga-Operations Per Second (GLOPs), setting a new possible standard for efficiency in skin lesion segmentation. The source code will be publicly available.

通过整合多层感知器和卷积神经网络，我们提出了一种高效低成本的UCM-Net解决方案，用于皮肤病变分割，其参数不超过50KB且每秒运算次数不超过0.05 Giga-Operations，为皮肤病变分割领域的高效性设立了新的可能标准。

UCM-Net：使用MLP和CNN的轻量级高效皮肤病变分割解决方案