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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Melanoma Breslow Thickness Classification Using Ensemble-Based Knowledge Distillation With Semi-Supervised

Juan P Dominguez-Morales, Juan-Carlos Hernandez-Rodriguez, Lourdes Duran-Lopez

IEEE Journal of Biomedical and Health Informatics

|September 20, 2024

Summary

This summary is machine-generated.

Semi-supervised learning improves artificial intelligence models for melanoma classification and Breslow thickness prediction, offering a valuable second opinion for dermatologists. This AI approach aids in distinguishing early-stage from invasive melanomas, enhancing diagnostic accuracy.

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Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Melanoma poses a significant global health challenge, causing over 90% of skin cancer deaths.
Accurate discrimination between in situ and invasive melanoma via dermoscopy is difficult, even for experts.
Artificial intelligence (AI) in medical image analysis offers potential for supporting dermatologists' diagnostic decisions.

Purpose of the Study:

To train and evaluate deep learning models for classifying in situ versus invasive melanoma.
To assess AI models for predicting Breslow thickness, a key melanoma prognostic factor.
To compare the efficacy of supervised and semi-supervised learning approaches in melanoma diagnosis.

Main Methods:

Utilized four diverse datasets for training and evaluating deep learning models.
Employed a multi-teacher ensemble knowledge distillation approach for semi-supervised learning.
Implemented a stratified 5-fold cross-validation scheme for robust evaluation.

Main Results:

Semi-supervised learning models outperformed supervised models in both melanoma classification and Breslow thickness prediction.
The best semi-supervised model achieved an AUC of 0.8547 for in situ vs. invasive melanoma classification.
On an external test set, semi-supervised methods demonstrated superior performance across all classification tasks.

Conclusions:

Semi-supervised learning significantly enhances the performance of AI models for melanoma classification and thickness prediction.
AI-powered diagnostic systems can serve as a valuable second opinion or triage tool for medical professionals.
Further development of AI in dermoscopy can improve diagnostic accuracy and patient outcomes for melanoma.