UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation

TL;DR

UltraLBM-UNet is a lightweight, efficient skin lesion segmentation model that combines bidirectional Mamba-based global modeling with local feature perception, achieving state-of-the-art accuracy with minimal computational resources.

Contribution

The paper introduces UltraLBM-UNet, a novel lightweight U-Net variant with bidirectional Mamba-based global modeling and a hybrid knowledge distillation strategy for resource-efficient skin lesion segmentation.

Findings

Achieves state-of-the-art accuracy on ISIC datasets

Uses only 0.034M parameters and 0.060 GFLOPs

Distilled ultra-compact model maintains competitive performance

Abstract

Skin lesion segmentation is a crucial step in dermatology for guiding clinical decision-making. However, existing methods for accurate, robust, and resource-efficient lesion analysis have limitations, including low performance and high computational complexity. To address these limitations, we propose UltraLBM-UNet, a lightweight U-Net variant that integrates a bidirectional Mamba-based global modeling mechanism with multi-branch local feature perception. The proposed architecture integrates efficient local feature injection with bidirectional state-space modeling, enabling richer contextual interaction across spatial dimensions while maintaining computational compactness suitable for point-of-care deployment. Extensive experiments on the ISIC 2017, ISIC 2018, and PH2 datasets demonstrate that our model consistently achieves state-of-the-art segmentation accuracy, outperforming existing lightweight and Mamba counterparts with only 0.034M parameters and 0.060 GFLOPs. In addition, we introduce a hybrid knowledge distillation strategy to train an ultra-compact student model, where the distilled variant UltraLBM-UNet-T, with only 0.011M parameters and 0.019 GFLOPs, achieves competitive segmentation performance. These results highlight the suitability of UltraLBM-UNet for point-of-care deployment, where accurate and robust lesion analyses are essential. The source code is publicly available at https://github.com/LinLinLin-X/UltraLBM-UNet.