Dermatology

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45 papers7 papers in the last four weeks, up 133% on the four weeks before. 0.0% of all new papers.

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
Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology
Oct 6, 2026Linda Wermelinger, Simone Lionetti, Fabian Gröger +6Skin Lesion Classification Explainable Medical Image Analysis
University of Basel · Lucerne University of Applied Sciences and Arts · University Hospital of Basel
Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification
Oct 2, 2026Emanoel dos Santos, Kelvin Cunha, Rodrigo Mota +8Skin Lesion Classification Dermatology
Centro de Informática, Universidade Federal de Pernambuco (UFPE), Brazil · Hospital das Clinicas, Ebserh, UFPE, Pernambuco, Brazil
How Medical VLMs Underutilize Their Vision Encoders: A Dermatology Perspective
Sep 29, 2026Janet Wang, Yunbei Zhang, Xiao Wang +1Medical Diagnosis VLM Interpretability
Tulane University · Oak Ridge National Laboratory
CAMEO: A Class-Activation-Mapped Equitable Overlay Framework for Fair and Robust Deep Learning-based Skin Condition Diagnosis
Sep 28, 2026Youssef Attia, Debasmita MukherjeeSkin Lesion Classification Algorithmic Fairness
Department of Electrical and Computer Engineering, University of New Brunswick, Fredericton, NB, Canada
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
Benchmarking Off-the-Shelf Multimodal AI Models Against Dermatologists on Patient-Captured Skin Images
Sep 21, 2026Rian Dolphin, Laura KnowlesSkin Lesion Classification Medical VLMs
Independent Research, Dublin, Ireland · School of Medicine, University of Limerick, Ireland
Deep Learning for Biopsy-Free Subtyping of Basal Cell Carcinoma from Dermatoscopic Images
Sep 7, 2026Alexandros Papadopoulos, Chrysa Episkopou, Ioannis Sarafis +2Skin Lesion Classification Cancer Subtype Classification
School of Electrical and Computer Engineering Aristotle University of Thessaloniki, Greece · First Department of Dermatology Aristotle University of Thessaloniki, Greece
Beyond Blur: A Semantic Tri-view Pipeline for Teledermatology Gradability via Skin Micro-relief
Sep 2, 2026Robert EngelExplainable Medical Image Analysis Image Quality Assessment
Avalytiq San Francisco, USA
Evidence-Driven Differential Diagnosis of Malignant Melanoma
Aug 28, 2026Naren Akash, Anirudh Kaushik, Jayanthi SivaswamySkin Lesion Classification Dermatology
Center for Visual Information Technology International Institute of Information Technology Hyderabad, India
MIFR: A Modality-Invariant and Fair Representation Framework for Skin Disease Classification
Aug 19, 2026Asonyu Senge Njih, Yvan Guifo Fodjo, Vianney Kengne Tchendji +2Fairness in Medical Image Classification Skin Lesion Classification
Department of Mathematics and Computer Science, University of Dschang, Cameroon · EFREI Research Lab, Université Paris-Panthéon-Assas, France · MIS, Université de Picardie Jules Verne, France +1
Open-Linguistic Concept Unified Learning for Cross-Site Interpretable Dermatology Image Diagnosis
Aug 4, 2026Chengyu Wu, Junpeng Tan, Wanxiang Luo +3Concept Bottleneck Models Medical Diagnosis
Zhejiang University Hangzhou, China · Westlake University Hangzhou, China · Harbin Institute of Technology Shenzhen, China +1
Interpretable Image-Level Acne Severity Grading via EfficientNet-B0 Transfer Learning and Grad-CAM
Jul 29, 2026Sophie Zeng, Sean Kalaycioglu, Collin Hong +1Medical Diagnosis Medical Image Classification
Dr. Robot Inc., Toronto, ON, Canada · Department of Aerospace Engineering, Toronto Metropolitan University, Toronto, ON, Canada · Skinopathy Inc., Toronto, ON, Canada
DermDepth: Toward Monocular Metric Scale 3D Reconstruction Models for Dermatology
Jul 14, 2026Héctor Carrión, Narges NorouziMedical Imaging 3D Reconstruction
University of California, Santa Cruz, USA · University of California, Berkeley, USA
Controllable Generation of Diverse Dermatological Imagery for Fair and Efficient Malignancy Classification
Jul 14, 2026Héctor Carrión, Narges NorouziControllable Image Generation Synthetic Data Augmentation
University of California, Santa Cruz, USA · University of California, Berkeley, USA
IViT: A Novel Interpretable Visual Transformer for Skin Disease Detection
Jun 22, 2026Haibiao Li, Di Lin, Xue Jiang +3Skin Lesion Classification Vision Transformer
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, 610054, P. R. China · Department of Dermatology, Chongqing Traditional Chinese Medicine Hospital, Chongqing, 400021, P. R. China · Chongqing Health Center for Women and Children, Chongqing, 400013, P. R. China
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
When LLMs Analyze Scars: From Images to Clinically-Meaningful Features
Jun 16, 2026Ruman Wang, Hangting YeMedical Diagnosis Medical Image Classification
Liaoning University of Traditional Chinese Medicine · School of Artificial Intelligence, Jilin University
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
How Indian Dermatologists are Utilizing Artificial Intelligence for Clinical Practice and Workflow Management: A Nationwide Survey with a Special Focus on atopic dermatitis
Jun 3, 2026Dipayan Sengupta, Saumya Panda, Sandipan Dhar +3Healthcare Workflow Automation
Charnock Hospital, Kolkata, India · Department of Dermatology, Jagannath Gupta Institute of Medical Sciences and Hospital, Kolkata, India · Department of Pediatric Dermatology, Institute of Child Health, Kolkata-700017, India +3
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 SereginMedical Image 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
Methodology for Creating a Clinically Verified Dermoscopic Image Dataset
May 24, 2026Kozachok Elena SergeevnaTraining Data Curation Medical Image Benchmarks
Ivannikov Institute for System Programming of RAS, Moscow, Russia
Privacy-Preserving Local Language Models for Longitudinal Data Retrieval in Chronic Dermatologic Disease: Implementation in Pemphigus Patients
May 24, 2026Abdurrahim Yilmaz, Ayşe Esra Koku Aksu, Duygu Yamen +5Privacy-Preserving Language Models Healthcare
Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London · Department of Dermatology and Venereology, Istanbul Research and Training Hospital · Department of Dermatology and Venereology, Istanbul Medeniyet University +1
DermAgent: A Self-Reflective Agentic System for Dermatological Image Analysis with Multi-Tool Reasoning and Traceable Decision-Making
May 14, 2026Yize Liu, Siyuan Yan, Ming Hu +5Medical Image Analysis LLM Agent Orchestration
AIM for Health Lab, Faculty of Information Technology, Monash University, Melbourne, Australia · Faculty of Information Technology, Monash University, Melbourne, Australia · University College London, Institute of Ophthalmology, London, United Kingdom
Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
May 1, 2026Roy Jiang, Hyunjae Kim, Zhenyue Qin +8Differential Diagnosis Clinical Triage
Department of Dermatology, Yale School of Medicine, Yale University, New Haven, CT, USA · Department of Bioinformatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA · Yale School of Medicine, Yale University, New Haven, CT, USA
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
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
Assessing Multimodal Chronic Wound Embeddings with Expert Triplet Agreement
Mar 31, 2026Fabian Kabus, Julia Hindel, Jelena Bratulić +6Medical Image Retrieval Multimodal Embedding
Institute of Medical Biometry and Statistics (IMBI), Medical Faculty and Medical Center, University of Freiburg · Department of Computer Science, Faculty of Engineering, University of Freiburg · Department of Dermatology, Medical Faculty and Medical Center, University of Freiburg

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Less Is More: A Leakage-Controlled Study of Dermoscopic Preprocessing for Joint Skin Lesion Classification and Segmentation with YOLO26

Truong Viet Vu, Nguyen Chi Hai, Nguyen Phuc Nguyen, Ngo Hoang Tu+2

Faculty of Information Technology, Van Lang School of Technology, Van Lang University, Ho Chi Minh City, Vietnam

Skin Lesion Classification Medical Image Segmentation Skin Lesion Segmentation Dermatology

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Handcrafted preprocessing is widely employed in automated dermoscopic analysis to suppress imaging artifacts and enhance lesion visibility. Nevertheless, its actual contribution to modern real-time models remains unclear, particularly when evaluation protocols do not adequately control correlations among images of the same lesion. This study presents a leakage-controlled, lesion-disjoint evaluation of dermoscopic preprocessing and augmentation for joint multi-class lesion classification and instance segmentation using a fixed nano-scale YOLO26 segmentation model (YOLO26n-seg). From HAM10000 (10,015 images), quality control yields 10,013 valid image-mask pairs from 7,468 unique lesions, partitioned into mutually exclusive sets by lesion identity. With the architecture, resolution, training budget, and evaluation protocol held fixed, we compare minimally processed images plus online augmentation against offline class balancing, DullRazor-CLAHE preprocessing, and raw-processed hybrid views, over three random seeds. On the lesion-disjoint test set, the raw baseline achieves a mask mAP

_{50:95}

0.5636 \pm 0.0234

, a Dice score of

0.9356 \pm 0.0024

, and a macro-F1 score of

0.6917 \pm 0.0202

. Offline augmentation does not improve the mean performance, while the combined and hybrid strategies reduce both class-aware segmentation and classification accuracy. At only 2.69 million parameters, the model runs at approximately 50 frames per second. Under a leakage-controlled, lesion-disjoint protocol with all non-input factors held fixed, minimally processed dermoscopic images combined with standard online augmentation deliver a better accuracy-efficiency trade-off than increasingly complex deterministic preprocessing, which yields no consistent joint benefit across three seeds on HAM10000.