Year: 2025
Type: article
Abstract: Melanoma subtyping plays a vital role in histopathological diagnosis, informing prognosis and, in some cases, guiding targeted therapy. However, conventional histologic classification is constrained by inter-rater reliability, morphologic overlap, and the underrepresentation of rare subtypes. Deep learning (DL)-particularly convolutional neural networks (CNNs)-presents a compelling opportunity to enhance diagnostic precision and reproducibility through automated analysis of histopathologic slides. This review examines the clinical importance and diagnostic challenges of melanoma subtyping, outlines core DL methodologies in dermatopathology, and synthesizes current advances in applying DL to subtype classification. Pertinent limitations including dataset imbalance, a lack of interpretability, and domain generalizability are discussed. Additionally, emerging directions such as multimodal integration, synthetic data generation, federated learning, and explainable AI are highlighted as potential solutions. As these technologies mature, DL holds considerable promise in advancing melanoma diagnostics and supporting more personalized, accurate, and equitable patient care.
Source: Dermatopathology
Authors: Dorra Guermazi, Sarina Khemchandani, Samer Wahood, Cuong Nguyen, Elie Saliba
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,810
OpenAlex ID: W4416123442