7 papers · 1 filter
Picking the Right Image to Classify: Reliable-Input Selection in Teledermatology
Fabian Gröger, Marco Weishaupt, Philippe Gottfrois +6
Dermatology models face distribution shifts in teledermatology settings, where submitted images differ from the training data in lighting, angle, distance, focus, and framing. Thes…
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…
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…
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…
Intrinsic Self-Supervision for Data Quality Audits
Fabian Gröger, Simone Lionetti, Philippe Gottfrois +6
Benchmark datasets in computer vision often contain off-topic images, near duplicates, and label errors, leading to inaccurate estimates of model performance. In this paper, we rev…