Harri Merisaari1, Pekka Taimen2, Otto Ettala2, Juha Knaapila2, Kari T Syvänen2, Esa Kähkönen2, Aida Steiner2, Janne Verho2, Paula Vainio2, Marjo Seppänen3, Jarno Riikonen4, Sanna Mari Vimpeli4, Antti Rannikko5, Outi Oksanen5, Tuomas Mirtti5, Ileana Montoya Perez1, Tapio Pahikkala1, Parisa Movahedi1, Tarja Lamminen2, Jani Saunavaara2, Peter J Boström2, Hannu J Aronen1, and Ivan Jambor6
1University of Turku, Turku, Finland, 2Turku University Hospital, Turku, Finland, 3Satakunta Central Hospital, Pori, Finland, 4Tampere University Hospital, Tampere, Finland, 5Helsinki University Hospital, Helsinki, Finland, 6Icahn School of Medicine at Mount Sinai, New York, NY, United States
1University of Turku, Turku, Finland, 2Turku University Hospital, Turku, Finland, 3Satakunta Central Hospital, Pori, Finland, 4Tampere University Hospital, Tampere, Finland, 5Helsinki University Hospital, Helsinki, Finland, 6Icahn School of Medicine at Mount Sinai, New York, NY, United States
PRORAD is a series of
machine learning challenges hosted at CodaLab which provide access to prostate
MRI data sets from 4 centers performed using a publicly available IMPROD bpMRI acquisition
protocol.
Figure 1 Phases of the machine learning challenge.
1) Data, Pre-processing and Feature extraction has been done. 2) Participant
need to pre-process feature data with method of choice, including imputation of
missing data, build a classifier with selected features, and validate the
results with data from Site I. 3) External validation with unseen data from
centers 1 and 2, with a limited number of evaluations. 4) The final test of the
trained model and machine learning process with unseen data from centers 1 and
2, and completely unseen centers III and IV.
Figure 2 Data management of the machine
learning (ML) challenge. Radiomic feature values and ground truth values are
both given for Training/Validation of ML model (A). Only radiomic feature
values are given for Leaderboard evaluations (B), where results are given five
times to allow minor edits to the ML, and in final evaluation (D), where only
one submission is done (C). Leaderboard and Test set (B-D) ground truth labels
are hidden inside the Codalab server where performance evaluations take place
by running python code through Docker package.