4 citations · 5 across the 5 of their papers we have counts for
5 papers
Point Tracking as a Temporal Cue for Robust Myocardial Segmentation in Echocardiography Videos
Bahar Khodabakhshian, Nima Hashemi, Armin Saadat +6
Purpose: Myocardium segmentation in echocardiography videos is a challenging task due to low contrast, noise, and anatomical variability. Traditional deep learning models either pr…
ControlEchoSynth: Boosting Ejection Fraction Estimation Models via Controlled Video Diffusion
Nima Kondori, Hanwen Liang, Hooman Vaseli +5
Synthetic data generation represents a significant advancement in boosting the performance of machine learning (ML) models, particularly in fields where data acquisition is challen…
CCSI: Continual Class-Specific Impression for Data-free Class Incremental Learning
Sana Ayromlou, Teresa Tsang, Purang Abolmaesumi +1
In real-world clinical settings, traditional deep learning-based classification methods struggle with diagnosing newly introduced disease types because they require samples from al…
EchoGNN: Explainable Ejection Fraction Estimation with Graph Neural Networks
Masoud Mokhtari, Teresa Tsang, Purang Abolmaesumi +1
Ejection fraction (EF) is a key indicator of cardiac function, allowing identification of patients prone to heart dysfunctions such as heart failure. EF is estimated from cardiac u…
Class Impression for Data-free Incremental Learning
Sana Ayromlou, Purang Abolmaesumi, Teresa Tsang +1
Standard deep learning-based classification approaches require collecting all samples from all classes in advance and are trained offline. This paradigm may not be practical in rea…