A glimpse of ClinicaDL, an open-source software for reproducible deep learning in neuroimaging

Elina Thibeau-Sutre, Mauricio Díaz, Ravi Hassanaly, Olivier Colliot, Ninon Burgos

Keywords: Deep learning, Neuroimaging, Data leakage, Reproducibility, Open-source

TL;DR: We present ClinicaDL, a deep learning software for neuroimaging aiming at bypassing common flaws of our domain.

Abstract: This paper presents ClinicaDL, a deep learning software for neuroimaging processing. Its aim is to provide a concrete solution to methodological flaws often found in our field (the difficult use of neuroimaging data sets, data leakage and insufficient reproducibility), but also to raise awareness and discuss these issues with our community. The corresponding journal paper was recently accepted in Computer Methods and Programs in Biomedicine.

Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.

Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.

Paper Type: recently published or submitted journal contributions

Primary Subject Area: Validation Study

Secondary Subject Area: Application: Radiology

Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.