11 citations · 11 across the 5 of their papers we have counts for
6 papers
Explicitly Aligned Multi-View Breast Cancer Risk Prediction from Variable-Length Screening Histories
Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin +5
Accurate breast cancer risk prediction from screening mammography is essential for improved risk stratification and personalized screening. It can benefit from both temporal breast…
Longitudinal Multi-View Breast Cancer Risk Prediction
Solveig Thrun, Zijun Sun, Suaiba A. Salahuddin +5
Accurate breast cancer risk prediction from screening mammography is critical for enabling personalized screening intervals and early detection. Recent deep learning methods have s…
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…
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…
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
Solveig Thrun, Stine Hansen, Zijun Sun +7
Regular mammography screening is essential for early breast cancer detection. Deep learning-based risk prediction methods have sparked interest to adjust screening intervals for hi…
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
Srishti Gautam, Ahcene Boubekki, Stine Hansen +4
The need for interpretable models has fostered the development of self-explainable classifiers. Prior approaches are either based on multi-stage optimization schemes, impacting the…