23 citations · 23 across the 5 of their papers we have counts for
5 papers
Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking
Thierry Judge, Nicolas Duchateau, Andreas Østvik +6
Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial motion tracking but often lack…
TSPFN: A Temporal Tabular Foundation Model for Physiological Time Series Classification
Jérémie Stym-Popper, Clément Rambour, Federica Granese +2
Designing models that generalize effectively in low- to medium-data regimes remains a primary challenge in medical machine learning, particularly for physiological time-series clas…
Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?
Thierry Judge, Nicolas Duchateau, Andreas Østvik +13
Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain…
DAFTED: Decoupled Asymmetric Fusion of Tabular and Echocardiographic Data for Cardiac Hypertension Diagnosis
Jérémie Stym-Popper, Nathan Painchaud, Clément Rambour +3
Multimodal data fusion is a key approach for enhancing diagnosis in medical applications. We propose an asymmetric fusion strategy starting from a primary modality and integrating…
Physics-Guided Neural Networks for Intraventricular Vector Flow Mapping
Hang Jung Ling, Salomé Bru, Julia Puig +6
Intraventricular vector flow mapping (iVFM) seeks to enhance and quantify color Doppler in cardiac imaging. In this study, we propose novel alternatives to the traditional iVFM opt…