The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data
Abstract
Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.HSUXJM-TNZF9CHSUXJM-TNZF9C
Cite
@article{arxiv.2306.10484,
title = {The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data},
author = {Luuk H. Boulogne and Julian Lorenz and Daniel Kienzle and Robin Schon and Katja Ludwig and Rainer Lienhart and Simon Jegou and Guang Li and Cong Chen and Qi Wang and Derik Shi and Mayug Maniparambil and Dominik Muller and Silvan Mertes and Niklas Schroter and Fabio Hellmann and Miriam Elia and Ine Dirks and Matias Nicolas Bossa and Abel Diaz Berenguer and Tanmoy Mukherjee and Jef Vandemeulebroucke and Hichem Sahli and Nikos Deligiannis and Panagiotis Gonidakis and Ngoc Dung Huynh and Imran Razzak and Reda Bouadjenek and Mario Verdicchio and Pasquale Borrelli and Marco Aiello and James A. Meakin and Alexander Lemm and Christoph Russ and Razvan Ionasec and Nikos Paragios and Bram van Ginneken and Marie-Pierre Revel Dubois},
journal= {arXiv preprint arXiv:2306.10484},
year = {2023}
}