GazeWorld autoregressively predicts latent representations of radiologist fixated patches with a spatial-completion branch to pretrain features that achieve SOTA supervised and zero-shot diagnostic accuracy on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax.
Us-jepa: A joint embedding predictive architecture for medical ultrasound
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A World Model of Radiologist Reading for Medical Image Representation Learning
GazeWorld autoregressively predicts latent representations of radiologist fixated patches with a spatial-completion branch to pretrain features that achieve SOTA supervised and zero-shot diagnostic accuracy on CheXpert, RSNA Pneumonia, and SIIM-ACR Pneumothorax.