Abstract

Obtaining large datasets with detailed annotations for medical imaging AI projects is a time consuming and expensive process as it usually requires the input of expert radiologists and pathologists. Collecting data to train outcome prediction models is even more challenging as the number of patients with both imaging and follow up data may be small, and only weak labels are available. This talk will describe several semi-supervised and self-supervised approaches which can make more efficient use of small and/or weakly labelled datasets.