# Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs

arXiv: https://arxiv.org/abs/2309.01899 · HTML: https://mlsanity.com/paper/2309.01899/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 5 Sep 2023.

## Builds on (most cited first, 17 of 17)

- [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019)
- [Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1710.05006/) (2017)
- [Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1605.01397/) (2016)
- [ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions](https://mlsanity.com/paper/2201.00382/) (2022)
- [Structural Entropy Guided Graph Hierarchical Pooling](https://mlsanity.com/paper/2206.13510/) (2022)
- [Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering](https://mlsanity.com/paper/2007.09990/) (2020)
- [SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization](https://mlsanity.com/paper/2303.09778/) (2023)
- [Unsupervised Foreground Extraction via Deep Region Competition](https://mlsanity.com/paper/2110.15497/) (2021)
- [Texture-guided Saliency Distilling for Unsupervised Salient Object Detection](https://mlsanity.com/paper/2207.05921/) (2022)
- [Unsupervised Domain Adaptive Salient Object Detection Through Uncertainty-Aware Pseudo-Label Learning](https://mlsanity.com/paper/2202.13170/) (2022)
- [A Generalization of Otsu's Method and Minimum Error Thresholding](https://mlsanity.com/paper/2007.07350/) (2020)
- [A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentation](https://mlsanity.com/paper/2107.04934/) (2021)
- [Hierarchical State Abstraction Based on Structural Information Principles](https://mlsanity.com/paper/2304.12000/) (2023)
- [Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles](https://mlsanity.com/paper/2304.00755/) (2023)
- [Supervised Saliency Map Driven Segmentation of the Lesions in Dermoscopic Images](https://mlsanity.com/paper/1703.00087/) (2017)
- [Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs](https://mlsanity.com/paper/2306.09950/) (2023)
- [Automatic skin lesion segmentation on dermoscopic images by the means of superpixel merging](https://mlsanity.com/paper/1808.06759/) (2018)

## Built on by (most cited first, 7 of 7)

- [SeBot: Structural Entropy Guided Multi-View Contrastive Learning for Social Bot Detection](https://mlsanity.com/paper/2405.11225/) (2024)
- [MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization](https://mlsanity.com/paper/2311.02880/) (2023)
- [Adversarial Socialbots Modeling Based on Structural Information Principles](https://mlsanity.com/paper/2312.08098/) (2023)
- [Effective Exploration Based on the Structural Information Principles](https://mlsanity.com/paper/2410.06621/) (2024)
- [Unsupervised Graph Clustering with Deep Structural Entropy](https://mlsanity.com/paper/2505.14040/) (2025)
- [USL-Net: Uncertainty Self-Learning Network for Unsupervised Skin Lesion Segmentation](https://mlsanity.com/paper/2309.13289/) (2023)
- [RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy](https://mlsanity.com/paper/2604.05594/) (2026)

## Abstract (arXiv)

Skin lesion segmentation is a fundamental task in dermoscopic image analysis. The complex features of pixels in the lesion region impede the lesion segmentation accuracy, and existing deep learning-based methods often lack interpretability to this problem. In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED. Specifically, skin lesions are segmented by minimizing the structural entropy of a superpixel graph constructed from the dermoscopic image. Then, we characterize the consistency of healthy skin features and devise a novel multi-scale segmentation mechanism by outlier detection, which enhances the segmentation accuracy by leveraging the superpixel features from multiple scales. We conduct experiments on four skin lesion benchmarks and compare SLED with nine representative unsupervised segmentation methods. Experimental results demonstrate the superiority of the proposed framework. Additionally, some case studies are analyzed to demonstrate the effectiveness of SLED.