We developed a transformer-based approach to efficiently merge information extracted from echocardiographic image sequences and data from electronic health records to learn the continuous representation of patients with hypertension.
We developed a physics-informed deep learning method to estimate vector flow mapping from color Doppler imaging. This strategy has the potential to facilitate the analysis of the blood flow during clinical exams.
We have developed a generic method for estimating aleatoric uncertainty based on contrastive modeling.
We are exploring domain adaptation strategies using reinforcement learning to enhance the generalizability of our segmentation models on echocardiographic images
We have developed a simulation pipeline to generate realistic synthetic echocardiographic sequences. This pipeline is currently being used to generate a large-scale cohort of virtual patients to feed deep learning methods.
We have developed a strategy based on learning the representation of plausible cardiac shapes to efficiently generate gold standard annotations. This pipeline has been successfully applied to two other tasks: the simulation of realistic echocardiographic sequences and the generalization of segmentation tools.
