Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging.

ABSTRACT: Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In clinical practice, the continuous progress of image acquisition technology or diagnostic procedures, the diversity of scanners, and evolving imaging protocols hamper the utility of machine learning, as prediction accuracy on new data deteriorates, or models become outdated due to these domain shifts. We propose a continual learning approach to deal with such domain shifts occurring at unknown time points. We adapt models to emerging variations in a continuous data stream while counteracting catastrophic forgetting. A dynamic memory enables rehear

SUBMITTER: Perkonigg M 

PROVIDER: S-EPMC8479083 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature