2 citations · 3 across the 20 of their papers we have counts for
22 papers
Consistent Distribution Matching for Data-Free Diffusion Distillation
Yuxiang Fu, Qi Yan, Zike Wu +4
Flow and diffusion models suffer from slow inference due to computationally expensive numerical integration. Distillation provides a promising way for a student model to learn from…
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…
Steering Geometry: Validating Human Value Geometry in LLM Steering Space
Mohammad Mahdi Abootorabi, Armin Saghafian, Ali Bazshoushtari +5
As large language models (LLMs) are increasingly deployed in alignment-sensitive contexts, activation steering has emerged as a lightweight, inference-time alternative to fine-tuni…
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…
ProtoTTA: Prototype-Guided Test-Time Adaptation
Mohammad Mahdi Abootorabi, Parvin Mousavi, Purang Abolmaesumi +1
Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balancing high accuracy with inhere…
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…
ProstNFound+: A Prospective Study using Medical Foundation Models for Prostate Cancer Detection
Paul F. R. Wilson, Mohamed Harmanani, Minh Nguyen Nhat To +7
Purpose: Medical foundation models (FMs) offer a path to build high-performance diagnostic systems. However, their application to prostate cancer (PCa) detection from micro-ultraso…
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…
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…
Federated Impression for Learning with Distributed Heterogeneous Data
Atrin Arya, Sana Ayromlou, Armin Saadat +2
Standard deep learning-based classification approaches may not always be practical in real-world clinical applications, as they require a centralized collection of all samples. Fed…
Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography
Ang Nan Gu, Michael Tsang, Hooman Vaseli +2
The fundamental problem with ultrasound-guided diagnosis is that the acquired images are often 2-D cross-sections of a 3-D anatomy, potentially missing important anatomical details…
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…