5 citations · 9 across the 13 of their papers we have counts for
15 papers
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 (…
P3CA: Encoder-Agnostic Interpretation of Vision Foundation Model Embeddings via Spatial Probing
Amoon Jamzad, Dilakshan Srikanthan, Faranak Akbarifar +2
Vision foundation models are increasingly used as reusable encoders in medical image computing, yet their high-dimensional spatial embeddings are difficult to inspect beyond downst…
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…
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…
A Dataset and Benchmarks for Atrial Fibrillation Detection from Electrocardiograms of Intensive Care Unit Patients
Sarah Nassar, Nooshin Maghsoodi, Sophia Mannina +7
Objective: Atrial fibrillation (AF) is the most common cardiac arrhythmia experienced by intensive care unit (ICU) patients and can cause adverse health effects. In this study, we…
Reducing Robotic Upper-Limb Assessment Time While Maintaining Precision: A Time Series Foundation Model Approach
Faranak Akbarifar, Nooshin Maghsoodi, Sean P Dukelow +2
Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40-64 reaches, imposing time and fatigue burdens. We evaluate whether…
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…
Domain Knowledge is Power: Leveraging Physiological Priors for Self Supervised Representation Learning in Electrocardiography
Nooshin Maghsoodi, Sarah Nassar, Paul F R Wilson +7
Objective: Electrocardiograms (ECGs) play a crucial role in diagnosing heart conditions; however, the effectiveness of artificial intelligence (AI)-based ECG analysis is often hind…
DualTrack: Sensorless 3D Ultrasound needs Local and Global Context
Paul F. R. Wilson, Matteo Ronchetti, Rüdiger Göbl +5
Three-dimensional ultrasound (US) offers many clinical advantages over conventional 2D imaging, yet its widespread adoption is limited by the cost and complexity of traditional 3D…
Diverse Prototypical Ensembles Improve Robustness to Subpopulation Shift
Minh Nguyen Nhat To, Paul F RWilson, Viet Nguyen +6
The subpopulationtion shift, characterized by a disparity in subpopulation distributibetween theween the training and target datasets, can significantly degrade the performance of…
TRUSWorthy: Toward Clinically Applicable Deep Learning for Confident Detection of Prostate Cancer in Micro-Ultrasound
Mohamed Harmanani, Paul F. R. Wilson, Minh Nguyen Nhat To +6
While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound…
Cinepro: Robust Training of Foundation Models for Cancer Detection in Prostate Ultrasound Cineloops
Mohamed Harmanani, Amoon Jamzad, Minh Nguyen Nhat To +11
Prostate cancer (PCa) detection using deep learning (DL) models has shown potential for enhancing real-time guidance during biopsies. However, prostate ultrasound images lack pixel…