5 papers
CleanPatrick: A Benchmark for Image Data Cleaning
Fabian Gröger, Simone Lionetti, Philippe Gottfrois +12
Robust machine learning depends on clean data, yet current image data cleaning benchmarks rely on synthetic noise or narrow human studies, limiting comparison and real-world releva…
A Global Atlas of Digital Dermatology to Map Innovation and Disparities
Fabian Gröger, Simone Lionetti, Philippe Gottfrois +7
The adoption of artificial intelligence in dermatology promises democratized access to healthcare, but model reliability depends on the quality and comprehensiveness of the data fu…
Clinical Uncertainty Impacts Machine Learning Evaluations
Simone Lionetti, Fabian Gröger, Philippe Gottfrois +4
Clinical dataset labels are rarely certain as annotators disagree and confidence is not uniform across cases. Typical aggregation procedures, such as majority voting, obscure this…
Towards Scalable Foundation Models for Digital Dermatology
Fabian Gröger, Philippe Gottfrois, Ludovic Amruthalingam +5
The growing demand for accurate and equitable AI models in digital dermatology faces a significant challenge: the lack of diverse, high-quality labeled data. In this work, we inves…
PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa
Philippe Gottfrois, Fabian Gröger, Faly Herizo Andriambololoniaina +13
Africa faces a huge shortage of dermatologists, with less than one per million people. This is in stark contrast to the high demand for dermatologic care, with 80% of the paediatri…