摘要：人工智能（AI）对微超声（μUS）的分析已展现出前列腺癌（PCa）检测的潜力。然而，现有的大多数AI方法仅孤立地分析单张μUS图像。相反，专家μUS读片者通常会评估完整的录制视频研究，这提供了三维上下文，从而相较单帧分析提高前列腺癌检测能力。受此临床工作流程启发，我们提出Compass，一种新颖的AI方法，将μUS研究建模为2D图像流。Compass联合整合前列腺旋转扫查视频与活检时刻采集的μUS帧，并利用以探头旋转角度为条件的变压器在整个研究中进行证据聚合。最后，解码器头预测患者的帧级和研究级风险评分。该模型使用多中心临床试验数据集进行训练和评估，该数据集包含前列腺连续旋转扫查和活检采集期间的视频。我们将所提方法与文献中的基线AI方法以及临床专家提供的风险评分进行比较。我们的框架表现出强劲性能，凸显了多视图上下文对μUS前列腺癌检测的价值，并为基于μUS的前列腺癌诊断提供了辅助人类专业知识的潜在强大工具。我们的代码见：此链接。

Abstract: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: this https URL.