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Challenges drive the state-of-the-art of automated medical image analysis.The quantity of public training data that they provide can limit theperformance of their solutions. Public access to the training methodology forthese solutions remains absent. This study implements the Type Three (T3)challenge format, which allows for training solutions on private data andguarantees reusable training methodologies. With T3, challenge organizers traina codebase provided by the participants on sequestered training data. T3 wasimplemented in the STOIC2021 challenge, with the goal of predicting from acomputed tomography (CT) scan whether subjects had a severe COVID-19 infection,defined as intubation or death within one month. STOIC2021 consisted of aQualification phase, where participants developed challenge solutions using2000 publicly available CT scans, and a Final phase, where participantssubmitted their training methodologies with which solutions were trained on CTscans of 9724 subjects. The organizers successfully trained six of the eightFinal phase submissions. The submitted codebases for training and runninginference were released publicly. The winning solution obtained an area underthe receiver operating characteristic curve for discerning between severe andnon-severe COVID-19 of 0.815. The Final phase solutions of all finalistsimproved upon their Qualification phase solutions.HSUXJM-TNZF9CHSUXJM-TNZF9C

The STOIC2021 COVID-19 AI challenge: applying reusable training methodologies to private data