# Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification

Source: [Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification](<https://www.sota2.com/research/paper/68fa7a371e209131cea81d6f>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-26T05:00:04.994Z

Authors: Ciro Listone, Aniello Murano

Published: 2025-06-23T08:49:57.000Z

## Abstract

Melanoma is a highly aggressive skin cancer, making early and accurate diagnosis critical. While deep learning excels in skin lesion classification, standard \`\`black-box" models struggle to explain diagnostic uncertainty, limiting clinical trust. This work introduces a hybrid framework combining a class-aware adversarial Variational Autoencoder and an XGBoost classifier, transcending simple binary classification by leveraging a generative latent space for interpretable decision support. Guided by adversarial training, the model learns the visual characteristics of skin lesions and projects them into a continuous latent space, ensuring that similar images are grouped closely together. Trained on this latent space, the XGBoost classifier achieves a robust AUC of 0.868, competing closely with state-of-the-art models. For borderline cases, the framework enables clinicians to leverage the latent topology through Content-Based Image Retrieval. This provides a dual benefit: it allows the clinician to visually compare an ambiguous lesion against biopsy-confirmed precedents and acts as an early warning sign since a borderline classification can indicate that a lesion shares features of both nevi and melanomas, potentially requiring close monitoring. Our approach translates algorithmic hesitation into transparent, evidence-based visual support, bridging the gap between predictive performance and clinical trust.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Dermatological Image Classification on ISIC 2019 (test)](<https://www.sota2.com/research/sota/dermatological-image-classification-on-isic-2019-test.md>): Dermatological Image Classification; ISIC 2019 (test); AUC: 0.8689.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2506.18414>)

[PDF](<https://arxiv.org/pdf/2506.18414.pdf>)