UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN
This provides an efficient diagnostic tool for skin cancer in resource-constrained settings, though it is incremental as it builds on existing segmentation methods.
The paper tackles skin lesion segmentation by introducing UCM-Net, a lightweight model combining MLP and CNN, which achieves robust performance with fewer than 50KB parameters and less than 0.05 GLOPs.
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.