Hyperbolic Metric Learning for Visual Outlier Detection

no code implementations • 22 Mar 2024 • Alvaro Gonzalez-Jimenez, Simone Lionetti, Dena Bazazian, Philippe Gottfrois, Fabian Gröger, Marc Pouly, Alexander Navarini

Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications.

Metric Learning Outlier Detection +1

Towards Reliable Dermatology Evaluation Benchmarks

1 code implementation • 13 Sep 2023 • Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Matthew Groh, Roxana Daneshjou, Labelling Consortium, Alexander A. Navarini, Marc Pouly

Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates.

Robust T-Loss for Medical Image Segmentation

1 code implementation • 1 Jun 2023 • Alvaro Gonzalez-Jimenez, Simone Lionetti, Philippe Gottfrois, Fabian Gröger, Marc Pouly, Alexander Navarini

Our results provide strong evidence that the T-Loss is a promising alternative for medical image segmentation where high levels of noise or outliers in the dataset are a typical phenomenon in practice.

Image Segmentation Medical Image Segmentation +2

SelfClean: A Self-Supervised Data Cleaning Strategy

no code implementations • 26 May 2023 • Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Alvaro Gonzalez-Jimenez, Ludovic Amruthalingam, Labelling Consortium, Matthew Groh, Alexander A. Navarini, Marc Pouly

Most benchmark datasets for computer vision contain irrelevant images, near duplicates, and label errors.

Self-Supervised Learning