2 citations · 3 across the 20 of their papers we have counts for
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Weakly Supervised Spatial Grounding for Discriminative Attention-Based Ultrasound-Histopathology Alignment in Prostate Cancer Grading
Obed Korshie Dzikunu, Emma Willis, Mohammad Mahdi Abootorabi +7
Unpaired cross-modal distillation transfers grade structure from histopathology into a micro-ultrasound (micro-US) encoder by aligning a pooled needle-region embedding to a frozen…
Learning Prostate Anatomy at Test Time for Cancer Detection in Micro-Ultrasound
Obed Korshie Dzikunu, Mohammad Mahdi Abootorabi, Mohamed Harmanani +7
Domain shift across clinical centers using different imaging hardware or acquisition protocols remains a fundamental barrier to deploying deep learning models for prostate cancer (…
Compass: Prostate Cancer Detection Needs Multi-View Context
Paul F. R. Wilson, Mohamed Harmanani, Zhuoxin Guo +6
Artificial intelligence (AI) analysis of micro-ultrasound (US) has shown promise for prostate cancer (PCa) detection. However, most existing AI methods focus on the analysis of…
Learning Where to Look: A Reinforcement Learning Framework for Robust Micro-Ultrasound Prostate Cancer Detection
Mohammad Mahdi Abootorabi, Sina Namazi, Armin Saadat +7
Micro-ultrasound (US) is a new, emerging, and promising imaging modality for prostate cancer (PCa) detection, but accurate identification of suspicious tissue remains highly dep…
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…
Vision-Language Models Encode Clinical Guidelines for Concept-Based Medical Reasoning
Mohamed Harmanani, Bining Long, Zhuoxin Guo +6
Concept Bottleneck Models (CBMs) are a prominent framework for interpretable AI that map learned visual features to a set of meaningful concepts for task-specific downstream predic…
GUIDE-US: Grade-Informed Unpaired Distillation of Encoder Knowledge from Histopathology to Micro-UltraSound
Emma Willis, Tarek Elghareb, Paul F. R. Wilson +6
Purpose: Non-invasive grading of prostate cancer (PCa) from micro-ultrasound (micro-US) could expedite triage and guide biopsies toward the most aggressive regions, yet current mod…
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…
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…
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…
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…