UCM-Net：基于MLP和CNN的轻量高效皮肤病变分割解决方案
UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN
AI总结：
UCM-Net是一种结合MLP和CNN的轻量高效皮肤病变分割模型，在PH2、ISIC 2017和ISIC 2018数据集上以少于50KB参数和低于0.05 GLOPs的计算量实现稳健性能，适合资源受限环境部署。
摘要：
皮肤癌构成了重大的公共卫生挑战，需要高效的诊断工具。我们引入了UCM-Net，一种结合多层感知器（MLP）和卷积神经网络（CNN）的新型皮肤病变分割模型。这种轻量、高效的架构，不同于传统的UNet设计，大幅降低了计算需求，使其非常适合移动健康应用。在PH2、ISIC 2017和ISIC 2018数据集上评估，UCM-Net展示了稳健的性能，参数少于50KB，且每秒运算量（GLOPs）低于0.05。此外，其在CPU环境中的最小内存需求仅为1.19MB。它是皮肤病变分割效率方面的潜在基准，适合在资源受限环境中部署。为了促进该领域的可访问性和进一步研究，UCM-Net源代码位于https://github.com/chunyuyuan/UCM-Net。
英文摘要：
Skin cancer poses a significant public health challenge, necessitating efficient diagnostic tools. We introduce UCM-Net, a novel skin lesion segmentation model combining Multi-Layer Perceptrons (MLP) and Convolutional Neural Networks (CNN). This lightweight, efficient architecture, deviating from traditional UNet designs, dramatically reduces computational demands, making it ideal for mobile health applications. Evaluated on PH2, ISIC 2017, and ISIC 2018 datasets, UCM-Net demonstrates robust performance with fewer than 50KB parameters and requires less than 0.05 Giga Operations Per Second (GLOPs). Moreover, its minimal memory requirement is just 1.19MB in CPU environment positions. It is a potential benchmark for efficiency in skin lesion segmentation, suitable for deployment in resource-constrained settings. In order to facilitate accessibility and further research in the field, the UCM-Net source code is https://github.com/chunyuyuan/UCM-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
