11 citations · 14 across the 11 of their papers we have counts for
5 papers · 1 filter
Explaining deep learning for ECG using time-localized clusters
Ahcène Boubekki, Konstantinos Patlatzoglou, Joseph Barker +2
Deep learning has significantly advanced electrocardiogram (ECG) analysis, enabling automatic annotation, disease screening, and prognosis beyond traditional clinical capabilities.…
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…
SuperCM: Improving Semi-Supervised Learning and Domain Adaptation through differentiable clustering
Durgesh Singh, Ahcène Boubekki, Robert Jenssen +1
Semi-Supervised Learning (SSL) and Unsupervised Domain Adaptation (UDA) enhance the model performance by exploiting information from labeled and unlabeled data. The clustering assu…
Supercm: Revisiting Clustering for Semi-Supervised Learning
Durgesh Singh, Ahcene Boubekki, Robert Jenssen +1
The development of semi-supervised learning (SSL) has in recent years largely focused on the development of new consistency regularization or entropy minimization approaches, often…
EnLVAM: Enhanced Left Ventricle Linear Measurements Utilizing Anatomical Motion Mode
Durgesh K. Singh, Ahcene Boubekki, Qing Cao +3
Linear measurements of the left ventricle (LV) in the Parasternal Long Axis (PLAX) view using B-mode echocardiography are crucial for cardiac assessment. These involve placing 4-6…