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"The STOIC2021 COVID-19 AI challenge: applying reusable training ..."

Luuk H. Boulogne et al. (2023)

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DOI: 10.48550/ARXIV.2306.10484

access: open

type: Informal or Other Publication

metadata version: 2023-11-12

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  - journals/corr/abs-2306-10484
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  - https://dblp.org/rec/journals/corr/abs-2306-10484
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schön, Katja Ludwig, Rainer Lienhart, Simon Jegou, Guang Li, Cong Chen, Qi Wang, Derik Shi, Mayug Maniparambil, Dominik Müller, Silvan Mertes, Niklas Schröter, Fabio Hellmann, Miriam Elia, Ine Dirks, Matías Nicolás Bossa, Abel Díaz Berenguer, Tanmoy Mukherjee, Jef Vandemeulebroucke, Hichem Sahli, Nikos Deligiannis, Panagiotis Gonidakis, Ngoc Dung Huynh, Imran Razzak, Mohamed Reda Bouadjenek, Mario Verdicchio, Pasquale Borrelli, Marco Aiello, James A. Meakin, Alexander Lemm, Christoph Russ, Razvan Ionasec, Nikos Paragios, Bram van Ginneken, Marie-Pierre Revel Dubois:
The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data. CoRR abs/2306.10484 (2023)

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