Compass: Prostate Cancer Detection Needs Multi-View Context

Artificial intelligence (AI) analysis of micro-ultrasound (μUS) has shown promise for prostate cancer (PCa) detection. However, most existing AI methods focus on the analysis of single μUS images in isolation. By contrast, expert μUS readers typically assess a full recorded video study, which provides three-dimensional context, to improve PCa detection compared to single-frame analysis. Inspired by this clinical workflow, we propose Compass, a novel AI methodology which models a μUS study as a stream of 2D images. Compass jointly integrates rotational sweep videos of the prostate with μUS frames acquired at the moment of biopsy, and performs evidence aggregation across the study using a transformer conditioned on the probe's rotational angle. Finally, a decoder head predicts frame-level and study-level risk scores for the patient. The model is trained and evaluated using a multi-center clinical trial dataset of μUS studies, including continuous rotational scans of the prostate and videos captured during biopsy acquisition. We compare the proposed method to baseline AI methods from the literature and to risk scores provided by clinical experts. Our framework shows strong performance, highlighting the value of multi-view context for μUS PCa detection, and providing a potentially powerful tool to complement human expertise in μUS-based PCa diagnosis. Our code is available at: https://github.com/mharmanani/Compass.