Active Transfer Learning for 3D Hippocampus Segmentation. - Ji Wu; Zhongfeng Kang; Sebastian Nørgaard Llambias; Mostafa Mehdipour-Ghazi; Mads Nielsen (MILLanD@MICCAI, 2023)

Heterogeneous Learning for Brain Lesion Segmentation, Detection, and Classification. - Sebastian Nørgaard Llambias; Mads Nielsen; Mostafa Mehdipour-Ghazi (NLDL, 2024)

Yucca: A Deep Learning Framework For Medical Image Analysis. - Sebastian Nørgaard Llambias; Julia Machnio; Asbjørn Munk; Jakob Ambsdorf; Mads Nielsen; Mostafa Mehdipour-Ghazi (CoRR, 2024)

AMAES: Augmented Masked Autoencoder Pretraining on Public Brain MRI Data for 3D-Native Segmentation. - Asbjørn Munk; Jakob Ambsdorf; Sebastian Nørgaard Llambias; Mads Nielsen (CoRR, 2024)

Data Augmentation-Based Unsupervised Domain Adaptation in Medical Imaging. - Sebastian Nørgaard Llambias; Mads Nielsen; Mostafa Mehdipour-Ghazi (SCIA (1), 2025)

A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning. - Asbjørn Munk; Stefano Cerri; Jakob Ambsdorf; Julia Machnio; Sebastian Nørgaard Llambias; Vardan Nersesjan; Christian Hedeager Krag; Peirong Liu; Pablo Rocamora-García; Mostafa Mehdipour-Ghazi; Mikael Boesen; Michael Eriksen Benros; et al. (CoRR, 2025)

General Methods Make Great Domain-specific Foundation Models: A Case-study on Fetal Ultrasound. - Jakob Ambsdorf; Asbjørn Munk; Sebastian Nørgaard Llambias; Anders Nymark Christensen; Kamil Wojciech Mikolaj; Randall Balestriero; Martin Grønnebæk Tolsgaard; Aasa Feragen; Mads Nielsen (CoRR, 2025)

Towards Scalable and Robust White Matter Lesion Localization via Multimodal Deep Learning. - Julia Machnio; Sebastian Nørgaard Llambias; Mads Nielsen; Mostafa Mehdipour-Ghazi (CoRR, 2025)