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Updated: Feb 4, 2026

Preparation and Using Phantom Lesions to Practice Fine Needle Aspiration Biopsies
Published on: September 29, 2009
Automated Classification of Skin Lesions: From Pixels to Practice
Akhila Narla1, Brett Kuprel2, Kavita Sarin3
1Stanford School of Medicine, Stanford University, Stanford, California, USA.
Abstract:
The letters "Interpretation of the Outputs of Deep Learning Model trained with Skin Cancer Dataset" and "Automated Dermatological Diagnosis: Hype or Reality?" highlight the opportunities, hurdles, and possible pitfalls with the development of tools that allow for automated skin lesion classification. The potential clinical impact of these advances relies on their scalability, accuracy, and generalizability across a range of diagnostic scenarios.
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