164 citations · 413 across the 24 of their papers we have counts for
10 papers · 1 filter
Keypoint Counting Classifiers: Turning Vision Transformers into Self-Explainable Models Without Training
Kristoffer Wickstrøm, Teresa Dorszewski, Siyan Chen +3
Current approaches for designing self-explainable models (SEMs) require complicated training procedures and specific architectures which makes them impractical. With the advance of…
The Impact of Longitudinal Mammogram Alignment on Breast Cancer Risk Assessment
Solveig Thrun, Stine Hansen, Zijun Sun +8
Regular mammography screening is crucial for early breast cancer detection. By leveraging deep learning-based risk models, screening intervals can be personalized, especially for h…
Fast Voxel-Wise Kinetic Modeling in Dynamic PET using a Physics-Informed CycleGAN
Christian Salomonsen, Samuel Kuttner, Michael Kampffmeyer +4
Tracer kinetic modeling serves a vital role in diagnosis, treatment planning, tracer development and oncology, but burdens practitioners with complex and invasive arterial input fu…
Random Window Augmentations for Deep Learning Robustness in CT and Liver Tumor Segmentation
Eirik A. Østmo, Kristoffer K. Wickstrøm, Keyur Radiya +3
Contrast-enhanced Computed Tomography (CT) is important for diagnosis and treatment planning for various medical conditions. Deep learning (DL) based segmentation models may enable…
Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models
Suaiba Amina Salahuddin, Teresa Dorszewski, Marit Almenning Martiniussen +7
Understanding what deep learning (DL) models learn is essential for the safe deployment of artificial intelligence (AI) in clinical settings. While previous work has focused on pix…
WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements
Durgesh Kumar Singh, Qing Cao, Sarina Thomas +3
Clinical guidelines recommend performing left ventricular (LV) linear measurements in B-mode echocardiographic images at the basal level -- typically at the mitral valve leaflet ti…