Researcher at the Laboratory of Imaging Technologies (University of Ljubljana, Faculty of Electrical Enginnering, Slovenia)

Vladyslav Zalevskyi, ..., Domen Preložnik, Žiga Špiclin, et al.: Advances in automated fetal brain MRI segmentation and biometry: insights from the FeTA 2024 challenge. Medical Image Analysis, 109:103941, 2026 [doi:10.1016/j.media.2026.103941 ] [COBISS-ID:265974787 ]

Domen Preložnik, Žiga Špiclin: Cross-modality white matter lesion segmentation by modality de-indentification. Pattern Recognition Letters, 199:120-127, 2026 [doi:10.1016/j.patrec.2025.11.020 ] [COBISS-ID:257288963 ]

Domen Preložnik, Žiga Špiclin: Improving brain MRI segmentation with multi-stage deep domain unlearning. 7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, Held in Conjunction with MICCAI 2024, Oct 6, Marrakesh, Morocco, Lecture Notes in Computer Science 15155:99-110, 2025 [doi:10.1007/978-3-031-74561-4_9 ] [COBISS-ID:212946179 ]

Domen Preložnik, Žiga Špiclin: Domain unlearning boosts lesion segmentation performance on seen and unseen MR scanner data. 9th International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, BrainLes SWITCH 2023, Held in Conjunction with MICCAI 2023, Oct 8 & 12, Vancouver, BC, Canada, Lecture Notes in Computer Science 14668:48-56, 2024 [doi:10.1007/978-3-031-76160-7_5 ]

Lara Dular, Domen Preložnik, Gregor Brecl Jakob, Lina Savšek, Jožef Magdič, Bojan Rojc, Žiga Špiclin: Future MS disease progression prediction using deep learning on conventional MRI scans. ACTRIMS Forum 2023, Feb 23-25, San Diego, USA, Multiple Scleroris Journal 29(Suppl 2):151, 2023 [doi:10.1177/13524585231169437 ] [COBISS-ID:163992067 ]