MultiTask learning AI system to assist BCC diagnosis with dual explanation
Universidad de Sevilla · Hospital Universitario Virgen Macarena · +1 more institution
Abstract
Basal cell carcinoma (BCC) accounts for 75% of all skin cancers. Currently, all major public hospitals in Spain have a dermatology care protocol that includes teledermatology. This has created an overload for hospital dermatologists, which could be alleviated with an AI tool for prioritization. Several AI systems have been proposed for this purpose, but the lack of transparent diagnostic explanations limits their clinical acceptance and implementation. This fact motivates the present study, which aims to develop an AI tool focused on detecting BCC from dermoscopic images incorporating dermatologist diagnostic criteria to enhance reliability. Specifically, the BCC diagnostic criterium is that a lesion is not…
Citation impact
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Authors
6Topics & keywords
- Interpretability
- Artificial intelligence
- Skin cancer
- Cancer
- Computer science
- Machine learning
- Medicine
- Internal medicine