Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning

arXiv:2405.01469v1 Announce Type: new
Abstract: AI Foundation models are gaining traction in various applications, including medical fields like radiology. However, medical foundation models are often tested on limited tasks, leaving their generalisability and biases unexplored. We present RayDINO, a large visual encoder trained by self-supervision on 873k chest X-rays. We compare RayDINO to previous state-of-the-art models across nine radiology tasks, from classification and dense segmentation to text generation, and provide an in depth analysis of population, age and sex biases …

abstract ai foundation ai foundation models analysis applications arxiv biases cs.ai cs.cv encoder fields foundation however human human-centric medical radiology ray robust self-supervised learning supervised learning supervision tasks through type visual x-ray