Generative AI for enhanced skin cancer diagnosis, dermatologist training, and patient education
Abstract
The early detection and monitoring of suspicious skin lesions are essential for effective dermatological diagnosis and treatment, particularly in understanding the progression of nevi to melanoma. To advance this understanding, we developed a simulation framework that models the transformation of nevi into melanoma using Cycle-Consistent Generative Adversarial Networks and frame interpolation to generate a detailed dataset of simulated lesion progressions. Optical flow analysis was applied to these dermoscopic image sequences, providing quantitative insights into lesion transformations and dynamic changes. Heatmap visualizations and optical flow vectors highlighted regions of significant activity and confirmed the fidelity of the simulated transformations. Additionally, we demonstrate how this new approach can visually complement existing textual explainable AI methods in dermatology, enhancing interpretability and trust in diagnostic outcomes. These findings represent a significant step toward improving dermatological diagnostics, patient education, and the early detection of melanoma.
Details
- Organisationseinheit(en)
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Hannoversches Zentrum für Optische Technologien (HOT)
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme
- Externe Organisation(en)
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Coronis Computing S.L.
University of Girona
- Typ
- Aufsatz in Konferenzband
- Publikationsdatum
- 19.03.2025
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Elektronische, optische und magnetische Materialien, Atom- und Molekularphysik sowie Optik, Biomaterialien, Radiologie, Nuklearmedizin und Bildgebung
- Ziele für nachhaltige Entwicklung
- SDG 3 - Gute Gesundheit und Wohlergehen
- Elektronische Version(en)
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https://doi.org/10.1117/12.3042664 (Zugang:
Geschlossen
)
