Modeling Multi-annotator Uncertainty as Multi-class Segmentation Problem. - Martin Zukovec; Lara Dular; Ziga Spiclin (BrainLes@MICCAI (1), 2021)

Improving Across Dataset Brain Age Predictions Using Transfer Learning. - Lara Dular; Ziga Spiclin (PRIME@MICCAI, 2021)

Mixup Augmentation Improves Age Prediction from T1-Weighted Brain MRI Scans. - Lara Dular; Ziga Spiclin (PRIME@MICCAI, 2022)

Predicting future multiple sclerosis disease progression from MR scans. - Lara Dular; Gregor Brecl-Jakob; Lina Savsek; Jozef Magdic; Ziga Spiclin (Computer-Aided Diagnosis, 2023)

Extensive T1-weighted MRI preprocessing improves generalizability of deep brain age prediction models. - Lara Dular; Franjo Pernus; Ziga Spiclin (Comput. Biol. Medicine, 2024)

QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge. - Hongwei Bran Li; Fernando Navarro; Ivan Ezhov; Amirhossein Bayat; Dhritiman Das; Florian Kofler; Suprosanna Shit; Diana Waldmannstetter; Johannes C. Paetzold; Xiaobin Hu; Benedikt Wiestler; Lucas Zimmer; et al. (CoRR, 2024)

BASE: Brain Age Standardized Evaluation. - Lara Dular; Ziga Spiclin; Alzheimer's Disease Neuroimaging Initiative (NeuroImage, 2024)