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

Luuk H. Boulogne et al. (2024)

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DOI: 10.1016/J.MEDIA.2024.103230

access: closed

type: Journal Article

metadata version: 2024-07-15

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  - journals/mia/BoulogneLKSLLJLCWSMMMSHEDBBMVSDGHRB24
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  - https://dblp.org/rec/journals/mia/BoulogneLKSLLJLCWSMMMSHEDBBMVSDGHRB24
Luuk H. Boulogne, Julian Lorenz, Daniel Kienzle, Robin Schön, Katja Ludwig, Rainer Lienhart, Simon Jégou, 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:
The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data. Medical Image Anal. 97: 103230 (2024)

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