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

Understand

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 of our model.