7 papers
HypOProto: Hyperbolic Ordinal Prototypes for Left Ventricular Filling Pressure Classification
Victoria Wu, Nima Hashemi, Hooman Vaseli +3
Echocardiography (echo) is a widely used imaging modality for assessing cardiac function, with Left Ventricular Filling Pressure (LVFP) serving as a critical physiological marker f…
Optimizing Point-of-Care Ultrasound Video Acquisition for Probabilistic Multi-Task Heart Failure Detection
Armin Saadat, Nima Hashemi, Bahar Khodabakhshian +4
Purpose: Echocardiography with point-of-care ultrasound (POCUS) must support clinical decision-making under tight bedside time and operator-effort constraints. We introduce a perso…
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…
EchoAgent: Guideline-Centric Reasoning Agent for Echocardiography Measurement and Interpretation
Matin Daghyani, Lyuyang Wang, Nima Hashemi +8
Purpose: Echocardiographic interpretation requires video-level reasoning and guideline-based measurement analysis, which current deep learning models for cardiac ultrasound do not…
TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction
Diane Kim, Minh Nguyen Nhat To, Sherif Abdalla +3
Coronary angiography remains the gold standard for diagnosing Acute Coronary Syndrome (ACS). However, its resource-intensive and invasive nature can expose patients to procedural r…
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…