Skin Lesion Classification

Latest papers 53

10 papers in the last four weeks, up 11% on the four weeks before. 0.1% of all new papers.

Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation
Jun 11, 2026Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok +2Skin Lesion Classification Probability Calibration
Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), Moscow, Russia · Orel Oncological Dispensary, Orel, Russia
Intelligent Skin Cancer Detection Using a Multispectral Metasurface and a Hybrid
Jun 9, 2026Afsane Saee ArezoomandSkin Lesion Classification Medical Image Classification
Young Researchers and Elite Club, Islamic Azad University, Urmia Branch, Urmia, Iran
Effect of Demographic Bias on Skin Lesion Classification
Jun 2, 2026Ralf Raumanns, Gerard Schouten, Veronika Cheplygina +1Skin Lesion Classification Algorithmic Bias
Fontys University of Applied Science, Venlo, The Netherlands · Eindhoven University of Technology, Eindhoven, The Netherlands · IT University of Copenhagen, Denmark +1
CoFiDA-M: Concept-Aware Feature Modulation for Cross-Domain Adaptation with Image-Only Inference
May 29, 2026Nurjahan Sultana, Moi Hoon Yap, Xinqi Fan +1Skin Lesion Classification Medical Image Classification
Department of Computing and Mathematics, Manchester Metropolitan University Dalton Building, Chester Street, M1 5GD Manchester, UK
Clinical Validation of the Melanoscope AI Mobile Dermoscopy Clinical Decision Support System
May 26, 2026Elena Sergeevna Kozachok, Sergey Sergeevich SereginSkin Lesion Classification Clinical Triage
1Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS),2026 25 Alexander Solzhenitsyn str., Moscow, 109004, Russian Federation · 2Orel Regional Oncology Dispensary,May 2 Ippodromny lane, Orel, Russian Federation
Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors
May 6, 2026Md. Safirur Rashid, Sabbir Ahmed, Muhammad Usama Islam +2Skin Lesion Classification Prototype-Based Classification
Department of Computer Science and Engineering, Islamic University of Technology, Gazipur, Bangladesh · Management Information Systems, Metropolitan State University, Minnesota, USA · School of Kinesiology, University of Louisiana at Lafayette, LA, USA
Synthetic Data Generation for Long-Tail Medical Image Classification: A Case Study in Skin Lesions
May 4, 2026Jiaxiang Jiang, Mahesh Subedar, Omesh TickooSkin Lesion Classification Synthetic Data Augmentation
Intel · Google
JI-ADF: Joint-Individual Learning with Adaptive Decision Fusion for Multimodal Skin Lesion Classification
Apr 30, 2026Phan Nguyen, Dat Cao, Hien Kha +4Skin Lesion Classification Medical Diagnosis
1KAIST · 2Taipei Medical University · 3Yale University
Concept Inconsistency in Dermoscopic Concept Bottleneck Models: A Rough-Set Analysis of the Derm7pt Dataset
Apr 21, 2026Gonzalo Nápoles, Isel Grau, Yamisleydi SalgueiroSkin Lesion Classification Concept Bottleneck Models
Department of Intelligent Systems, Tilburg University, 5037 AB Tilburg, The Netherlands · Information Systems Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands · Department of Industrial Engineering, Faculty of Engineering, Universidad de Talca, Campus Curic´o, Curic´o 3340000, Chile
Learning Superpixel Ensemble and Hierarchy Graphs for Melanoma Detection
Apr 4, 2026Asmaa M. Elwer, Muhammad A. Rushdi, Mahmoud H. AnnabySkin Lesion Classification Medical Image Classification
Department of Biomedical Engineering and Systems, Faculty of Engineering, Cairo University, Giza 12613, Egypt · School of Information Technology, New Giza University, Giza 12256, Egypt · Department of Mathematics, Faculty of Science, Cairo University, Giza 12613, Egypt
When AI and Experts Agree on Error: Intrinsic Ambiguity in Dermatoscopic Images
Apr 1, 2026Loris Cino, Pier Luigi Mazzeo, Alessandro Martella +3Skin Lesion Classification
Sapienza - Dipartimento di Ingegneria informatica, automatica e gestionale (DIAG) , V. Ariosto, 25, Rome, 00185, Lazio, Italy · Istituto di Scienze Applicate e Sistemi Intelligenti (ISASI), Consiglio Nazionale delle Ricerche (CNR), Via Monteroni s.n, Lecce, 73100, Italy · Dermatologia Myskin, Poliambulatorio Specialistico Medico-Chirurgico, Via S. Marco, 21, Tiggiano, 73030, Puglia, Italy +2
SkinGPT-X: A Self-Evolving Collaborative Multi-Agent System for Transparent and Trustworthy Dermatological Diagnosis
Mar 27, 2026Zhangtianyi Chen, Yuhao Shen, Florensia Widjaja +6Medical Diagnosis Skin Lesion Classification
School of Data Science, The Chinese University of Hong Kong, Shenzhen · Department of Dermatology, Tianjin Institute of Integrative Dermatology, Tianjin Academy of Traditional Chinese Medicine Affiliated Hospital, Tianjin 300120, China · Department of Dermatology, Beijing AnZhen Hospital, Capital Medical University, Beijing 100029, China..
An Image Dataset of Common Skin Diseases of Bangladesh and Benchmarking Performance with Machine Learning Models
Mar 26, 2026Sazzad Hossain, Saiful Islam, Muhammad Ibrahim +3Skin Lesion Classification Medical Image Classification
Department of Computer Science and Engineering, Faridpur Engineering College, Faridpur, Bangladesh · Department of Computer Science and Engineering, University of Dhaka, Dhaka, Bangladesh · Department of Oncology, Labaid Cancer and Super Speciality Center, Dhaka, Bangladesh +1
SkinCLIP-VL: Consistency-Aware Vision-Language Learning for Multimodal Skin Cancer Diagnosis
Mar 22, 2026Zhixiang Lu, Shijie Xu, Kaicheng Yan +6Medical VLMs VLM Adaptation
Xi’an Jiaotong-Liverpool University, China · University of Liverpool, United Kingdom
DermaFlux: Synthetic Skin Lesion Generation with Rectified Flows for Enhanced Image Classification
Mar 17, 2026Stathis Galanakis, Alexandros Koliousis, Stefanos ZafeiriouMedical Image Synthesis Synthetic Data Augmentation
Imperial College London, UK · Northeastern University London, UK
DERM-3R: A Resource-Efficient Multimodal Agents Framework for Dermatologic Diagnosis and Treatment in Real-World Clinical Settings
Mar 6, 2026Jirui Dai, Zhendong Wang, Chongjing Wang +7Multimodal Agents Clinical Decision-Making
Unsupervised Causal Prototypical Networks for De-biased Interpretable Dermoscopy Diagnosis
Feb 27, 2026Junhao Jia, Yueyi Wu, Huangwei Chen +4Explainable Medical Image Analysis Skin Lesion Classification
Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science and Technology, Zhejiang University · Hangzhou Dianzi University · Xi’an Jiaotong University
Contrastive meta-domain adaptation for robust skin lesion classification across clinical and acquisition conditions
Feb 23, 2026Rodrigo Mota, Kelvin Cunha, Emanoel dos Santos +6Skin Lesion Classification Domain Adaptation
Centro de Informática, Universidade Federal de Pernambuco, Brazil
DerMAE: Improving skin lesion classification through conditioned latent diffusion and MAE distillation
Feb 23, 2026Francisco Filho, Kelvin Cunha, Fábio Papais +6Skin Lesion Classification
Centro de Inform´atica, Universidade Federal de Pernambuco, Brazil
Deep Learning for Dermatology: An Innovative Framework for Approaching Precise Skin Cancer Detection
Feb 19, 2026Mohammad Tahmid Noor, B. M. Shahria Alam, Tasmiah Rahman Orpa +3Skin Lesion Classification Medical Diagnosis
Department of Computer Science and Engineering, East West University, Dhaka, Bangladesh
A Generative AI Approach for Reducing Skin Tone Bias in Skin Cancer Classification
Feb 16, 2026Areez Muhammed Shabu, Mohammad Samar Ansari, Asra AslamMedical Image Analysis Synthetic Data Augmentation
University of Sheffield, UK · University of Chester, UK
Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification
Jun 23, 2025Ciro Listone, Aniello MuranoVariational Autoencoders Skin Lesion Classification
University of Naples Federico II, Naples, Italy.
Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers
Dec 4, 2021Renzhen Wang, De cai, Kaiwen Xiao +3Skin Lesion Classification Hierarchical Classification
School of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an, China · Tencent AI Lab, Shenzhen, China · ByteDance, Shenzhen, China +4

Jun 11, 2026J/K move · Enter open · S save

Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation

Elena S. Kozachok, Sergey S. Seregin, Aleksandr V. Kozachok, Ilya P. Latyshev+1

Ivannikov Institute for System Programming of the Russian Academy of Sciences (ISP RAS), Moscow, Russia · Orel Oncological Dispensary, Orel, Russia

Skin Lesion Classification Probability Calibration Distribution Shift Clinical Triage Medical Image Classification

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Purpose. To compare deep learning architectures and classification schemes for dermoscopic images of skin neoplasms and assess their generalization on transfer from open international datasets to independent clinical datasets of Russian practice. Methods. Four architectures (ViT-B/16, Swin-S, ConvNeXt-S, EfficientNetV2-S) were compared in three schemes: binary (malignant/benign), single-stage four-class (benign, MEL, SCC, BCC), and a two-stage cascade (binary triage, then three-class differentiation MEL/SCC/BCC). All models used ImageNet-pretrained weights and a single augmentation protocol on aggregated open ISIC Archive data, and were evaluated on an internal held-out sample and two clinical datasets (Melanoscope AI mobile system; Sechenov University). Results. Internally the binary stage attains ROC-AUC 0.952-0.966; on Sechenov University it drops to 0.797-0.893, sensitivity to 0.53-0.67, and ECE rises from 0.02 to 0.27-0.39 with underestimation of malignancy, quantifying a generalization gap in ranking and calibration. Paired tests confirm one inter-architecture result on clinical data: the deficit of ViT-B/16 at the binary stage (p<0.05); at the differentiation stage no architecture has a proven advantage. The cascade raises macro F1 over single-stage four-class classification for most architectures, but significantly only for ViT-B/16, by recovering malignant lesions assigned to the dominant benign class. On ISIC MILK10k, direct 11-class classification yields mean-class sensitivity 0.525. Conclusion. A tunable triage threshold gives sensitivity control not attainable in standard single-stage (argmax) classification and better reproduces clinical differential-diagnosis logic. The persistent generalization gap mandates external clinical validation and recalibration before deployment.