4 citations · 11 across the 15 of their papers we have counts for
10 papers · 1 filter
Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology
Linda Wermelinger, Simone Lionetti, Fabian Gröger +6
The clinical integration of AI systems in digital dermatology relies heavily on human trust. Clinically interpretable visual concepts can act as intermediate representations enhanc…
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…
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…
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…
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…
Hyperbolic Metric Learning for Visual Outlier Detection
Alvaro Gonzalez-Jimenez, Simone Lionetti, Dena Bazazian +4
Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications. However, the inherent hierarchical concept structure of visual data,…
Towards Reliable Dermatology Evaluation Benchmarks
Fabian Gröger, Simone Lionetti, Philippe Gottfrois +6
Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data-cleaning protocol…
Robust T-Loss for Medical Image Segmentation
Alvaro Gonzalez-Jimenez, Simone Lionetti, Philippe Gottfrois +3
This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution…
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…