# A4-MR

MRI foundation models have largely learned image-level representations that struggle to separate normal from abnormal findings within one organ. A4-MR is pretrained in two stages on roughly 50k MRI studies and 491k series, pushing image-text alignment down to the anatomy level and learning separate embeddings for healthy and unhealthy anatomies. With a frozen encoder it gains 1.7 AUROC on abnormality detection over MedSigLIP, 1.4 Dice on knee segmentation over MedNeXt, stays within 0.4 Dice of the best model on abdominal organ segmentation, and localizes 3D anatomies without any prompt on par with MedSAM2 given a ground-truth box.

MRI foundation models have largely learned image-level representations that struggle to separate normal from abnormal findings within one organ. A4-MR is pretrained in two stages on roughly 50k MRI studies and 491k series, pushing image-text alignment down to the anatomy level and learning separate embeddings for healthy and unhealthy anatomies. With a frozen encoder it gains 1.7 AUROC on abnormality detection over MedSigLIP, 1.4 Dice on knee segmentation over MedNeXt, stays within 0.4 Dice of the best model on abdominal organ segmentation, and localizes 3D anatomies without any prompt on par with MedSAM2 given a ground-truth box.