# MoSSGate: Memory-Modulated State-Space Gating for Skin Lesion Segmentation

arXiv: https://arxiv.org/abs/2609.20181 · HTML: https://mlsanity.com/paper/2609.20181/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 42 references that are in the map.
- First posted on arXiv 24 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (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)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Long Range Language Modeling via Gated State Spaces](https://mlsanity.com/paper/2206.13947/) (2022)
- [UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer](https://mlsanity.com/paper/2109.04335/) (2021)
- [U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation](https://mlsanity.com/paper/2406.02918/) (2024)
- [Selective Structured State-Spaces for Long-Form Video Understanding](https://mlsanity.com/paper/2303.14526/) (2023)
- [S2-MLP: Spatial-Shift MLP Architecture for Vision](https://mlsanity.com/paper/2106.07477/) (2021)
- [TransAttUnet: Multi-level Attention-guided U-Net with Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2107.05274/) (2021)
- [MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation](https://mlsanity.com/paper/2211.01784/) (2022)
- [EGE-UNet: an Efficient Group Enhanced UNet for skin lesion segmentation](https://mlsanity.com/paper/2307.08473/) (2023)
- [CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion](https://mlsanity.com/paper/2308.01239/) (2023)
- [Skin Lesion Analysis Towards Melanoma Detection Using Deep Learning Network](https://mlsanity.com/paper/1703.00577/) (2017)
- [Dual Cross-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2303.17696/) (2023)
- [SlimConv: Reducing Channel Redundancy in Convolutional Neural Networks by Weights Flipping](https://mlsanity.com/paper/2003.07469/) (2020)
- [Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation](https://mlsanity.com/paper/2305.00678/) (2023)
- [UCM-Net: A Lightweight and Efficient Solution for Skin Lesion Segmentation using MLP and CNN](https://mlsanity.com/paper/2310.09457/) (2023)

## Abstract (arXiv)

Accurate skin lesion segmentation is crucial for reliable computer-aided dermatological diagnosis, yet existing convolutional and transformer-based models often struggle to jointly capture long-range spatial dependencies and fine boundary details under limited computational budgets. This trade-off between global context modeling and boundary-aware localization frequently leads to over-segmentation, fragmented predictions, or missing thin peripheral structures. To address this challenge, we propose MoSSGate, a plug-and-play module for U-Net that integrates (i) boundary-aware spatial gating to restrict long-range propagation to informative regions, (ii) an external memory modulator that provides sample-adaptive dynamic control, and (iii) parallel 2D state-space modeling for efficient global context aggregation with linear complexity. The proposed design enables adaptive, context-aware information propagation while preserving sharp and accurate lesion boundaries. Extensive experiments on the ISIC 2017 and ISIC 2018 benchmarks demonstrate state-of-the-art accuracy with strong efficiency, achieving 86.3% and 85.9% mIoU and 92.6% and 90.6% Dice, respectively, while requiring substantially fewer FLOPs than most competing CNN-based methods. These results highlight a favorable accuracy efficiency trade-off for high-resolution medical image segmentation.