# MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/2211.01784 · HTML: https://mlsanity.com/paper/2211.01784/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 37 references that are in the map.
- First posted on arXiv 3 Nov 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Multi-Scale Context Aggregation by Dilated Convolutions](https://mlsanity.com/paper/1511.07122/) (2015)
- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [TransBTS: Multimodal Brain Tumor Segmentation Using Transformer](https://mlsanity.com/paper/2103.04430/) (2021)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [DS-TransUNet:Dual Swin Transformer U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2106.06716/) (2021)
- [UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation](https://mlsanity.com/paper/2107.00781/) (2021)
- [Beyond Self-attention: External Attention using Two Linear Layers for Visual Tasks](https://mlsanity.com/paper/2105.02358/) (2021)
- [Shallow Attention Network for Polyp Segmentation](https://mlsanity.com/paper/2108.00882/) (2021)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection](https://mlsanity.com/paper/1703.00523/) (2017)

## Built on by (most cited first, 30 of 59)

- [VM-UNet: Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2402.02491/) (2024)
- [Vision Mamba: A Comprehensive Survey and Taxonomy](https://mlsanity.com/paper/2405.04404/) (2024)
- [EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation](https://mlsanity.com/paper/2307.08473/) (2023)
- [UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation](https://mlsanity.com/paper/2403.20035/) (2024)
- [VM-UNET-V2 Rethinking Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.09157/) (2024)
- [H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation](https://mlsanity.com/paper/2403.13642/) (2024)
- [U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation](https://mlsanity.com/paper/2311.17791/) (2023)
- [Spider: A Unified Framework for Context-dependent Concept Segmentation](https://mlsanity.com/paper/2405.01002/) (2024)
- [HC-Mamba: Vision MAMBA with Hybrid Convolutional Techniques for Medical Image Segmentation](https://mlsanity.com/paper/2405.05007/) (2024)
- [SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2409.00346/) (2024)
- [MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation](https://mlsanity.com/paper/2509.18493/) (2025)
- [SynergyNet: Bridging the Gap between Discrete and Continuous Representations for Precise Medical Image Segmentation](https://mlsanity.com/paper/2310.17764/) (2023)
- [Inspiring the Next Generation of Segment Anything Models: Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards Context-Dependent Concepts under Different Scenes](https://mlsanity.com/paper/2412.01240/) (2024)
- [TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation](https://mlsanity.com/paper/2604.00684/) (2026)
- [HMT-UNet: A hybird Mamba-Transformer Vision UNet for Medical Image Segmentation](https://mlsanity.com/paper/2408.11289/) (2024)
- [LV-UNet: A Lightweight and Vanilla Model for Medical Image Segmentation](https://mlsanity.com/paper/2408.16886/) (2024)
- [MUCM-Net: A Mamba Powered UCM-Net for Skin Lesion Segmentation](https://mlsanity.com/paper/2405.15925/) (2024)
- [A Lightweight U-like Network Utilizing Neural Memory Ordinary Differential Equations for Slimming the Decoder](https://mlsanity.com/paper/2412.06262/) (2024)
- [SkinMamba: A Precision Skin Lesion Segmentation Architecture with Cross-Scale Global State Modeling and Frequency Boundary Guidance](https://mlsanity.com/paper/2409.10890/) (2024)
- [Merging Context Clustering with Visual State Space Models for Medical Image Segmentation](https://mlsanity.com/paper/2501.01618/) (2025)
- [Hierarchical Self-Prompting SAM: A Prompt-Free Medical Image Segmentation Framework](https://mlsanity.com/paper/2506.02854/) (2025)
- [MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation](https://mlsanity.com/paper/2604.20286/) (2026)
- [Learning Multi-axis Representation in Frequency Domain for Medical Image Segmentation](https://mlsanity.com/paper/2312.17030/) (2023)
- [A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images](https://mlsanity.com/paper/2406.00492/) (2024)
- [Med-TTT: Vision Test-Time Training model for Medical Image Segmentation](https://mlsanity.com/paper/2410.02523/) (2024)
- [Generative Diffusion Model-based Compression of MIMO CSI](https://mlsanity.com/paper/2503.03753/) (2025)
- [FuseUNet: A Multi-Scale Feature Fusion Method for U-like Networks](https://mlsanity.com/paper/2506.05821/) (2025)
- [UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation](https://mlsanity.com/paper/2512.21584/) (2025)
- [UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN](https://mlsanity.com/paper/2310.09457/) (2023)
- [UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas](https://mlsanity.com/paper/2508.09339/) (2025)

## Abstract (arXiv)

Recently, some pioneering works have preferred applying more complex modules to improve segmentation performances. However, it is not friendly for actual clinical environments due to limited computing resources. To address this challenge, we propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity so far. Briefly, we propose four modules: (1) DGA consists of dilated convolution and gated attention mechanisms to extract global and local feature information; (2) IEA, which is based on external attention to characterize the overall datasets and enhance the connection between samples; (3) CAB is composed of 1D convolution and fully connected layers to perform a global and local fusion of multi-stage features to generate attention maps at channel axis; (4) SAB, which operates on multi-stage features by a shared 2D convolution to generate attention maps at spatial axis. We combine four modules with our U-shape architecture and obtain a light-weight medical image segmentation model dubbed as MALUNet. Compared with UNet, our model improves the mIoU and DSC metrics by 2.39% and 1.49%, respectively, with a 44x and 166x reduction in the number of parameters and computational complexity. In addition, we conduct comparison experiments on two skin lesion segmentation datasets (ISIC2017 and ISIC2018). Experimental results show that our model achieves state-of-the-art in balancing the number of parameters, computational complexity and segmentation performances. Code is available at https://github.com/JCruan519/MALUNet.