Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction

Guermazi, Dorra; Khemchandani, Sarina; Wahood, Samer; Nguyen, Cuong; Saliba, Elie

doi:10.3390/dermatopathology12040042

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Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction

by

Dorra Guermazi

^1,*

,

Sarina Khemchandani

¹,

Samer Wahood

¹

,

Cuong Nguyen

² and

Elie Saliba

^1,3

¹

Department of Dermatology, The Warren Alpert Medical School of Brown University, Providence, RI 02903, USA

²

Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA

³

Department of Dermatology, Gilbert and Rose-Marie Chagoury School of Medicine, Lebanese American University, Beirut 13-5053, Lebanon

^*

Author to whom correspondence should be addressed.

Dermatopathology 2025, 12(4), 42; https://doi.org/10.3390/dermatopathology12040042

Submission received: 11 August 2025 / Revised: 17 September 2025 / Accepted: 20 October 2025 / Published: 12 November 2025

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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.

Keywords: melanoma; deep learning; histopathology; melanoma subtypes; artificial intelligence; dermatopathology; convolutional neural networks; image analysis

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MDPI and ACS Style

Guermazi, D.; Khemchandani, S.; Wahood, S.; Nguyen, C.; Saliba, E. Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction. Dermatopathology 2025, 12, 42. https://doi.org/10.3390/dermatopathology12040042

AMA Style

Guermazi D, Khemchandani S, Wahood S, Nguyen C, Saliba E. Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction. Dermatopathology. 2025; 12(4):42. https://doi.org/10.3390/dermatopathology12040042

Chicago/Turabian Style

Guermazi, Dorra, Sarina Khemchandani, Samer Wahood, Cuong Nguyen, and Elie Saliba. 2025. "Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction" Dermatopathology 12, no. 4: 42. https://doi.org/10.3390/dermatopathology12040042

APA Style

Guermazi, D., Khemchandani, S., Wahood, S., Nguyen, C., & Saliba, E. (2025). Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction. Dermatopathology, 12(4), 42. https://doi.org/10.3390/dermatopathology12040042

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Translating Features to Findings: Deep Learning for Melanoma Subtype Prediction

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