Latent Space Analysis for Melanoma Prevention

2025-06-23

AI Summary

Melanoma exhibits rapid progression and high mortality, necessitating early, interpretable, and continuous risk assessment tools. Existing deep learning models typically yield only binary classifications and lack clinical interpretability. To address this, we propose an interpretable risk modeling framework based on a Conditional Variational Autoencoder (CVAE). First, we construct a structured semantic latent space that explicitly encodes morphological relationships among skin lesions. Second, we define a geometric risk metric grounded in latent-space distances—quantifying malignancy by measuring proximity to known melanoma samples. Finally, we integrate this metric with a Support Vector Machine (SVM) for precise benign–malignant classification. Our approach provides dual interpretability—both visual (via latent-space reconstructions) and geometric (via distance-based risk scores)—thereby enhancing model transparency and clinical trustworthiness. Empirical evaluation demonstrates robust performance in identifying ambiguous-border and high-risk cases.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Melanoma represents a critical health risk due to its aggressive progression and high mortality, underscoring the need for early, interpretable diagnostic tools. While deep learning has advanced in skin lesion classification, most existing models provide only binary outputs, offering limited clinical insight. This work introduces a novel approach that extends beyond classification, enabling interpretable risk modelling through a Conditional Variational Autoencoder. The proposed method learns a structured latent space that captures semantic relationships among lesions, allowing for a nuanced, continuous assessment of morphological differences. An SVM is also trained on this representation effectively differentiating between benign nevi and melanomas, demonstrating strong and consistent performance. More importantly, the learned latent space supports visual and geometric interpretation of malignancy, with the spatial proximity of a lesion to known melanomas serving as a meaningful indicator of risk. This approach bridges predictive performance with clinical applicability, fostering early detection, highlighting ambiguous cases, and enhancing trust in AI-assisted diagnosis through transparent and interpretable decision-making.

Problem

• Develop interpretable risk modeling for melanoma diagnosis
• Structured latent space captures semantic lesion relationships
• Enhance clinical trust via transparent AI decision-making

Innovation

• Conditional Variational Autoencoder for interpretable risk modeling
• Structured latent space captures semantic lesion relationships
• SVM trained on latent space enhances melanoma differentiation