Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings
Nooshin Mojab, Vahid Noroozi, Darvin Yi, Manoj Prabhakar Nallabothula, Abdullah Sagar Aleem, P.S. Yu, Joelle Hallak · 2020
With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. Deep learning models perform well when trained on standardized datasets from artificial settings. However, generalization and translation to real-world clinical settings are challenging. The complexity of real-world applications in healthcare emanates from different data distributions across multiple device domains, variations in image resolution, human errors, and the lack of manual grading. Moreover, healthcare applications not only suffer from scarcity in labeled data, but also face limited access to un-labeled data. These limitations pose additional challenges to developing translatable applications for clinical care. In this paper, we utilize self-supervised representation learning methods, formulated effectively in transfer learning settings, to address limited data availability and assess the importance of real-world data for generalizations to clinical settings. We show that by employing a self-supervised approach with transfer learning on a multi-domain real-world dataset, we can achieve 16% relative improvement on a standardized dataset over supervised baselines.