MICCAI 2020

2 Open Source Deep Learning Platform 18 DAILY Mo n d a y Why be an Open Source Deep Learning Developer? by Stephen Aylward (Kitware, Inc.), Jorge Cardoso (Kings College London) and Prerna Dogra (Nvidia) The field of deep learning has two dominant characteristics: outstanding performance and openness . While the outstanding performance of deep learning systems is well known, the openness of deep learning is perhaps less well publicized yet nevertheless equally important. The openness of deep learning refers to the dominance of open science practices in the field. It is standard practice to offer open publications (post on arXiv ), to share data ( ImageNet ), and to use and contribute to open source deep learning platforms ( PyTorch ). Furthermore, for deep learning research and development, this openness spans academia and industry, wherewe see the same platforms being used in research and commercial development. These open platforms include well established, general purpose systems such as PyTorch and the Insight Toolkit (ITK), as well as cutting-edge, domain specific systems such as the Medical Open Network for AI (MONAI) platform. These platforms allow the outstanding performance of deep learning systems to be readily shared, compared, commercialized, and extended. This dependence on open source software also represents a major change in attitude. The adage “you get what you pay for” no longer applies in this context. However, two major questions arise: Who is developing and maintaining the open source software of deep learning, and why do they do it? To answer that question, we interviewed several of the lead developers of MONAI and ITK: Nic Ma