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.
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.
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.
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.
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 • 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.
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.