5 papers
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 de…
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…
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…
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…