From the 1 of 9 linked papers with an AI index.
9 papers
Longitudinal Multi-View Breast Cancer Risk Prediction
Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin +5
The paper introduces LMV-Net, a deep learning model that jointly analyzes CC and MLO mammography views with explicit longitudinal alignment to improve breast cancer risk prediction…
ProtoX-AD: Self-Explainable Time Series Anomaly Detection and Characterization
Aitor Sánchez-Ferrera, Elisabeth Wetzer, Kristoffer Wickstrøm +2
Recent advances in time series anomaly detection (TSAD) have highlighted the effectiveness of self-supervised classification-based approaches. These methods apply transformations t…
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…
A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal FDG PET imaging
Christian Salomonsen, Luigi T Luppino, Fredrik Aspheim +7
Dynamic positron emission tomography (PET) and kinetic modeling are pivotal in advancing tracer development research in small animal studies. Accurate kinetic modeling requires pre…