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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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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging

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Advancing Dermatological Diagnostics: Interpretable AI for Enhanced Skin Lesion Classification.

Carlo Metta¹, Andrea Beretta¹, Riccardo Guidotti²

¹Institute of Information Science and Technologies (ISTI-CNR), 56124 Pisa, Italy.

Diagnostics (Basel, Switzerland)

|April 13, 2024

Summary

This summary is machine-generated.

Explainable Artificial Intelligence (XAI) methods are being developed to improve trust in AI for medical diagnosis. Tailored XAI for skin lesion classification enhances user confidence and aids in identifying critical diagnostic features.

Keywords:

AI in healthcare Explainable Artificial Intelligence adversial autoecnoders dermoscopic images skin image analysis

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Area of Science:

Medical Informatics
Artificial Intelligence in Medicine
Dermatology

Background:

Interpreting deep learning models in critical medical diagnosis is challenging.
Existing Explainable Artificial Intelligence (XAI) methods often lack specificity for complex medical tasks.
Automated AI decision-making systems require enhanced user trust, especially in diagnosing conditions like skin lesions.

Purpose of the Study:

To enhance user trust and confidence in AI diagnostic systems for skin lesions.
To tailor an XAI method for explaining AI classification of diverse skin lesion types.
To identify critical features influencing AI-driven skin lesion diagnosis.

Main Methods:

Developed a tailored XAI method for skin lesion diagnosis.
Utilized synthetic images of skin lesions as examples and counterexamples for explanation generation.
Conducted a validation survey with domain experts, novices, and laypersons to assess explanation effectiveness.

Main Results:

Explanations generated by the tailored XAI method significantly increased user trust and confidence in the AI system.
Exploration of the AI model's latent space revealed clear class separations for common skin lesions.
The findings suggest potential for improving diagnostic accuracy and correcting misdiagnoses.

Conclusions:

Tailored XAI methods can effectively enhance trust in AI for specialized medical applications like dermatology.
The developed XAI approach provides practitioners with insights into AI decision-making for skin lesion classification.
Understanding the AI model's latent space can reveal underlying diagnostic patterns and aid clinical practice.