2 citations · 2 across the 9 of their papers we have counts for
4 papers · 1 filter
ProtoEFNet: Dynamic Prototype Learning for Inherently Interpretable Ejection Fraction Estimation in Echocardiography
Yeganeh Ghamary, Victoria Wu, Hooman Vaseli +4
Ejection fraction (EF) is a crucial metric for assessing cardiac function and diagnosing conditions such as heart failure. Traditionally, EF estimation requires manual tracing and…
Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics
Ang Nan Gu, Michael Tsang, Hooman Vaseli +2
Computer-aided diagnosis systems must make critical decisions from medical images that are often noisy, ambiguous, or conflicting, yet today's models are trained on overly simplist…
PRECISE-AS: Personalized Reinforcement Learning for Efficient Point-of-Care Echocardiography in Aortic Stenosis Diagnosis
Armin Saadat, Nima Hashemi, Hooman Vaseli +5
Aortic stenosis (AS) is a life-threatening condition caused by a narrowing of the aortic valve, leading to impaired blood flow. Despite its high prevalence, access to echocardiogra…
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…