most citedAMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CV2025

Revisiting CLIP: Efficient Alignment of 3D MRI and Tabular Data using Domain-Specific Foundation Models

Jakob Krogh Petersen, Valdemar Licht, Mads Nielsen +1

Multi-modal models require aligned, shared embedding spaces. However, common CLIP-based approaches need large amounts of samples and do not natively support 3D or tabular data, bot…

eess.IV2024★ 1 cited

AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation

Asbjørn Munk, Jakob Ambsdorf, Sebastian Llambias +1

This study investigates the impact of self-supervised pretraining of 3D semantic segmentation models on a large-scale, domain-specific dataset. We introduce BRAINS-45K, a dataset o…