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.
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.
no code implementations • 11 Aug 2023 • Fabian Gröger, Marc Pouly, Flavia Tinner, Leif Brandes
Many hotels target guest acquisition efforts to specific markets in order to best anticipate individual preferences and needs of their guests.
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.
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.
no code implementations • 23 Apr 2021 • Fabian Gröger, Anne-Marie Rickmann, Christian Wachinger
We propose to predict the uncertainty of pseudo labels and integrate it in the training process with an uncertainty-guided loss function to highlight labels with high certainty.
1 code implementation • 10 Nov 2020 • Youssef Beauferris, Jonas Teuwen, Dimitrios Karkalousos, Nikita Moriakov, Mattha Caan, George Yiasemis, Lívia Rodrigues, Alexandre Lopes, Hélio Pedrini, Letícia Rittner, Maik Dannecker, Viktor Studenyak, Fabian Gröger, Devendra Vyas, Shahrooz Faghih-Roohi, Amrit Kumar Jethi, Jaya Chandra Raju, Mohanasankar Sivaprakasam, Mike Lasby, Nikita Nogovitsyn, Wallace Loos, Richard Frayne, Roberto Souza
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process.