Real-World Multi-Domain Data Applications for Generalizations to Clinical Settings

TL;DR

This paper demonstrates that self-supervised transfer learning on multi-domain real-world healthcare data significantly improves the generalization of machine learning models to clinical settings, addressing data scarcity and variability issues.

Contribution

It introduces a self-supervised transfer learning approach tailored for multi-domain healthcare data, enhancing model generalization to clinical environments.

Findings

Achieved 16% relative improvement over supervised baselines.

Verified the importance of diverse real-world data for clinical generalization.

Showed effectiveness of self-supervised methods in healthcare applications.

Abstract

With promising results of machine learning based models in computer vision, applications on medical imaging data have been increasing exponentially. However, generalizations to complex real-world clinical data is a persistent problem. Deep learning models perform well when trained on standardized datasets from artificial settings, such as clinical trials. However, real-world data is different and translations are yielding varying results. The complexity of real-world applications in healthcare could emanate from a mixture of different data distributions across multiple device domains alongside the inevitable noise sourced from varying image resolutions, human errors, and the lack of manual gradings. In addition, healthcare applications not only suffer from the scarcity of labeled data, but also face limited access to unlabeled data due to HIPAA regulations, patient privacy, ambiguity in data ownership, and challenges in collecting data from different sources. These limitations pose additional challenges to applying deep learning algorithms in healthcare and clinical translations. In this paper, we utilize self-supervised representation learning methods, formulated effectively in transfer learning settings, to address limited data availability. Our experiments verify the importance of diverse real-world data for generalization 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.