5 citations · 11 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
Calibrated Diverse Ensemble Entropy Minimization for Robust Test-Time Adaptation in Prostate Cancer Detection
Mahdi Gilany, Mohamed Harmanani, Paul Wilson +6
High resolution micro-ultrasound has demonstrated promise in real-time prostate cancer detection, with deep learning becoming a prominent tool for learning complex tissue propertie…