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20212026
most citedSelf-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI

5 citations · 9 across the 12 of their papers we have counts for

collaborators

13 papers

eess.IV2026

Technically Plausible but Clinically Misleading? Expert Evaluation of Patient-Personalized Synthetic Prostate MRI

Gabriel Paulo Maglalang Israel, Sol Gedde, Alvaro Fernandez-Quilez

Magnetic resonance imaging (MRI) is central to prostate cancer assessment, yet its acquisition is costly and time-consuming, making it a major bottleneck in the patient care pathwa…

eess.IV2026

Clinical Pathways Matter for Multimodal Deep Learning in Early Alzheimers Disease Detection

Yao Lu, Solveig Kristina Hammonds, Alvaro Fernandez-Quilez

Identifying individuals at risk of Alzheimer's disease (AD), particularly in the preclinical and early stages, remains challenging. Although deep learning approaches based on struc…

cs.CV2026

Neuropsychiatric Deviations From Normative Profiles: An MRI-Derived Marker for Early Alzheimer's Disease Detection

Synne Hjertager Osenbroch, Lisa Ramona Rosvold, Yao Lu +1

Neuropsychiatric symptoms (NPS) such as depression and apathy are common in Alzheimer's disease (AD) and often precede cognitive decline. NPS assessments hold promise as early dete…

eess.SP2025

Beyond the Signal: Medication State Effect on EEG-Based AI models for Parkinson's Disease

Anna Kurbatskaya, Fredrik Nilsen Låder, Andreas Solvang Nese +2

Parkinson's disease (PD) poses a growing challenge due to its increasing prevalence, complex pathology, and functional ramifications. Electroencephalography (EEG), when integrated…

eess.IV2023★ 2 cited

Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach

Tim Nikolass Lindeijer, Tord Martin Ytredal, Trygve Eftestøl +4

An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools conside…

eess.IV2023★ 2 cited

On undesired emergent behaviors in compound prostate cancer detection systems

Erlend Sortland Rolfsnes, Philip Thangngat, Trygve Eftestøl +4

Artificial intelligence systems show promise to aid in the di- agnostic pathway of prostate cancer (PC), by supporting radiologists in interpreting magnetic resonance images (MRI)…