A self-supervised text-vision framework for automated brain abnormality detection

arXiv:2405.02782v1 Announce Type: new
Abstract: Artificial neural networks trained on large, expert-labelled datasets are considered state-of-the-art for a range of medical image recognition tasks. However, categorically labelled datasets are time-consuming to generate and constrain classification to a pre-defined, fixed set of classes. For neuroradiological applications in particular, this represents a barrier to clinical adoption. To address these challenges, we present a self-supervised text-vision framework that learns to detect clinically relevant abnormalities in brain MRI scans by directly leveraging the rich …

abstract applications art artificial artificial neural networks arxiv automated brain classification cs.cv datasets detection expert framework generate however image image recognition medical networks neural networks recognition set state tasks text type vision