Skin Lesion Segmentation

Follow this topic Ask about this topic

Skin lesion segmentation separates a lesion from surrounding skin in dermoscopic images, providing masks for measuring shape and supporting downstream diagnosis. Common approaches build on U-Net-style convolutional models, with transformers and state-space models, boundary-aware features, uncertainty guidance, and pretrained foundation models used to capture detail and context; some methods also use pseudo-labels or user clicks to reduce annotation needs. Models are commonly evaluated on ISIC, PH2, and HAM10000, using overlap and boundary metrics, while newer evaluations examine unseen categories, skin-tone measures, and lesion-disjoint splits. A key challenge is balancing strong benchmark scores and efficient inference with reliable boundaries across low-contrast lesions, new cohorts, and differing acquisition conditions.

Latest papers

15 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

FedDermaSeg: Federated Learning for Dermatological Image Segmentation
Oct 6, 2026Anabik Pal, Ganesh Patidar, Bikash SantraMedical Image Segmentation Skin Lesion Segmentation
Department of Computer Science, Indian Institute of Science Education and Research Berhampur, India · Department of Mathematics, Indian Institute of Technology Jodhpur, India · School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India
Less Is More: A Leakage-Controlled Study of Dermoscopic Preprocessing for Joint Skin Lesion Classification and Segmentation with YOLO26
Oct 6, 2026Truong Viet Vu, Nguyen Chi Hai, Nguyen Phuc Nguyen +3Skin Lesion Classification Medical Image Segmentation
Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam
MDSkin-Net: Multi-Task Skin Lesion Analysis Driven by Pattern Analysis Priors and Spatial Alignment Regularization
Sep 25, 2026Yijian Li, Saad Bedros, Paul Bigliardi +3Skin Lesion Classification Skin Lesion Segmentation
University of Minnesota, Minneapolis, MN, USA
Initialization and Stopping Tolerance in CPU Dermoscopic Segmentation
Sep 22, 2026Wenhao Xu, Yixian Kong, Ting Pan +2Image Segmentation Medical Image Segmentation
Zhengzhou Police University, Zhengzhou, China · School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China · Qilu University of Technology and Jinan Supercomputing Center, Jinan, China
InstEditSeg: Instruction-Driven Image Editing for Polyp and Skin Lesion Segmentation
Sep 2, 2026Ziquan Liu, Zhewei Zhu, Xuyang ShiText-Guided Image Segmentation Polyp Segmentation
School of Information and Control Engineering, Southwest University of Science and Technology, Mianyang 621010, China
EA-LiteUNet: An Edge-Adaptive and Resource-Efficient U-Net for Boundary-Sensitive Dermoscopic Image Segmentation
Aug 16, 2026Wang Jiangtao, Nur Intan Raihana Ruhaiyem, Fu Panpan +2Image Segmentation Skin Lesion Segmentation
School of Network Communication, Zhejiang Yuexiu University, Shaoxing,312000, China · School of Computer Sciences, Universiti Sains Malaysia, Penang,11800, Malaysia
MLFFM-SegDiff: A Multi-Level Feature Fusion Diffusion Model for Skin Lesion Segmentation
Jun 25, 2026Jingjun Gu, Chaojie Shen, Yifeng Cao +3Multi-Scale Feature Fusion Diffusion Models for Medical Image Segmentation
Keyi College of Zhejiang Sci-Tech University, Shaoxing, China · Zhejiang Lanchen Digital Intelligence Technology Co., Ltd., Hangzhou, China · School of Software Technology, Zhejiang University, Hangzhou, China +2
PEFT-MedSAM: Efficient Fine-Tuning of Medical Foundation Models for Explainable Skin Lesion Segmentation
Jun 17, 2026Asad Channa, Abdullah Khan, Asghar Ali Chandio +4Medical Imaging Foundation Models Medical Image Segmentation
Department of Computer Science, Quaid-e-Awam University of Engineering, Sciences & Technology · Department of Artificial Intelligence, Quaid-e-Awam University of Engineering, Sciences & Technology, Pakistan · Department of Computer Science, Sindh Madressatul Islam University, City Campus, Karachi +1
Patch-MoE Mamba: A Patch-Ordered Mixture-of-Experts State Space Architecture for Medical Image Segmentation
May 18, 2026Diego Adame, Fabian Vazquez, Jose A. Nunez +7Mamba Image Segmentation
University of Texas Rio Grande Valley, Edinburg, TX 78539, USA
Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation
May 10, 2026Duwei Dai, Caixia Dong, Guowei Dai +6Medical Image Segmentation Skin Lesion Segmentation
Department of Dermatology, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, China. · National-Local Joint Engineering Research Center of Biodiagnosis & Biotherapy, the Second Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710004, Shaanxi, China. · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China. +3
MambaLiteUNet: Cross-Gated Adaptive Feature Fusion for Robust Skin Lesion Segmentation
Apr 22, 2026Md Maklachur Rahman, Soon Ki Jung, Tracy HammondMamba Multi-Scale Feature Fusion
Texas A&M University, College Station, TX, USA · Kyungpook National University, Daegu, South Korea
RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy
Apr 7, 2026Yujie Yao, Yuhaohang He, Junjie Huang +6Skin Lesion Segmentation Boundary-Aware Segmentation
College of Information Engineering, Sichuan Agricultural University, Ya’an, China
Exploring the Impact of Skin Color on Skin Lesion Segmentation
Mar 31, 2026Kuniko Paxton, Medina Kapo, Amila Akagić +3Fairness in Medical Image Classification Skin Lesion Segmentation
School of Digital and Physical Sciences at the University of Hull, Cottingham Road, Hull, HU6 7RX, United Kingdom · Faculty of Electrical Engineering at the University of Sarajevo, Zmaja od Bosne bb, Sarajevo, 71000, Bosnia and Herzegovina
ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation
Mar 12, 2026Lijun Guo, Qian Zhou, Zidi Shi +2Interactive Medical Image Segmentation Active Learning
School of Computer Science, Wuhan University, Wuhan, 430072, China · Dongguan Polytechnic, Dongguan, 523651, China
SGDC: Structurally-Guided Dynamic Convolution for Medical Image Segmentation
Feb 26, 2026Bo Shi, Wei-ping Zhu, M. N. S. SwamyMedical Image Segmentation Dynamic Convolution
Department of Electrical and Computer Engineering, Concordia University

Oct 6, 2026J/K move · Enter open · S save

FedDermaSeg: Federated Learning for Dermatological Image Segmentation

Anabik Pal, Ganesh Patidar, Bikash Santra

Department of Computer Science, Indian Institute of Science Education and Research Berhampur, India · Department of Mathematics, Indian Institute of Technology Jodhpur, India · School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India

Medical Image Segmentation Skin Lesion Segmentation Federated Learning

Open paper AskPDF arXiv

Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment planning. Automated skin lesion analysis can assist dermatologists, with lesion segmentation serving as a fundamental step in computer-aided diagnostic systems. Conventional deep learning-based segmentation models typically rely on centralized training, where images and their corresponding segmentation masks are collected on a central server. Such data aggregation raises privacy concerns in medical applications and requires substantial centralized computational resources. To address these limitations, we investigate the feasibility of federated learning for privacy-preserving skin lesion segmentation. The training and validation sets of the ISIC 2018 Skin Lesion Segmentation Challenge dataset are used to simulate a distributed learning environment and develop a federated segmentation model. The resulting model is evaluated on the ISIC 2018 test set and the PH2 dataset to assess its performance and generalizability. Experimental results demonstrate that the federated model achieves performance comparable to centralized training while consistently improving upon the locally trained models. These findings demonstrate the potential of federated learning for collaborative skin lesion segmentation without requiring centralized aggregation of medical images.