A Strong Baseline for Domain Adaptation and Generalization in Medical Imaging

Abstract

This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for generalization to out-of-sample domains.

arXiv commentExtended abstract of a journal submission

References in corpus (3)

• Confounding variables can degrade generalization performance of radiological deep learning models
• PadChest: A large chest x-ray image dataset with multi-label annotated reports
• Deep learning in radiology: an overview of the concepts and a survey of the state of the art

Cited by in corpus (5)

• The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
• Continual Learning for Domain Adaptation in Chest X-ray Classification
• Model-Based Domain Generalization
• Learning to estimate label uncertainty for automatic radiology report parsing
• Structured dataset documentation: a datasheet for CheXpert