8 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…
Representation-Based Data Quality Audits for Audio
Alvaro Gonzalez-Jimenez, Fabian Gröger, Linda Wermelinger +4
Data quality issues such as off-topic samples, near duplicates, and label errors often limit the performance of audio-based systems. This paper addresses these issues by adapting S…
Is Hyperbolic Space All You Need for Medical Anomaly Detection?
Alvaro Gonzalez-Jimenez, Simone Lionetti, Ludovic Amruthalingam +4
Medical anomaly detection has emerged as a promising solution to challenges in data availability and labeling constraints. Traditional methods extract features from different layer…
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…