Curia-MAE: Multi-Modal Multi-Anatomy MAE Pre-Training for 3D Medical Image Segmentation

TL;DR - Curia-MAE is a convolutional masked-autoencoder radiology foundation model pre-trained on 300,000 CT and MRI volumes across many anatomical sites, designed so a single frozen encoder can serve diverse 3D segmentation tasks. It matters because frozen pre-trained encoders have historically lagged nnU-Net, and closing that gap cuts the cost of adapting and deploying models in clinical workflows.

• Extends convolutional MAE pre-training with three additions: a robust reconstruction objective, a feature regularizer, and a local-global similarity objective.
• Multi-modal (CT + MRI) and multi-anatomy pre-training corpus of 300,000 images; evaluated on eight anatomy- and lesion-focused segmentation benchmarks.
• Improves frozen-encoder performance over a strong MAE baseline, stays competitive under full finetuning, and is superior on lesion tasks where labeled data is scarce.
• Authors state pre-trained weights will be released publicly; the paper targets dense prediction, which prior radiology foundation model evaluations underrepresent.