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This study introduces Compass, an AI methodology that enhances prostate cancer detection by analyzing micro-ultrasound (渭US) studies as a continuous stream of 2D images, rather than isolated frames. By integrating rotational sweep videos with biopsy-acquired 渭US frames, Compass employs a transformer model to aggregate evidence across the study, yielding both frame-level and study-level risk scores. The results demonstrate that this multi-view context significantly improves detection accuracy compared to traditional single-frame analysis and expert risk scores, underscoring its potential as a valuable tool in clinical practice.
Multi-view analysis of 渭US data can dramatically enhance prostate cancer detection, outperforming traditional methods and expert assessments.
Artificial intelligence (AI) analysis of micro-ultrasound ($\mu$US) has shown promise for prostate cancer (PCa) detection. However, most existing AI methods focus on the analysis of single $\mu$US images in isolation. By contrast, expert $\mu$US 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 $\mu$US study as a stream of 2D images. Compass jointly integrates rotational sweep videos of the prostate with $\mu$US 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 $\mu$US 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 $\mu$US PCa detection, and providing a potentially powerful tool to complement human expertise in $\mu$US-based PCa diagnosis. Our code is available at: https://github.com/mharmanani/Compass.
