A large body of previous machine learning methods for ultrasound-based prostate cancer detection classify small regions of interest (ROIs) of ultrasound signals that lie within a larger needle trace corresponding to a prostate tissue biopsy (called biopsy core). These ROI-scale models suffer from weak labeling as histopathology results available for biopsy cores only approximate the distribution of cancer in the ROIs. ROI-scale models do not take advantage of contextual information that are normally considered by pathologists, i.e. they do not consider information about surrounding tissue and larger-scale trends when identifying cancer. We aim to improve cancer detection by taking a multi-scale, i.e. ROI-scale and biopsy core-scale, approach. Methods: Our multi-scale approach combines (i) an "ROI-scale" model trained using self-supervised learning to extract features from small ROIs and (ii) a "core-scale" transformer model that processes a collection of extracted features from multiple ROIs in the needle trace region to predict the tissue type of the corresponding core. Attention maps, as a byproduct, allow us to localize cancer at the ROI scale. We analyze this method using a dataset of micro-ultrasound acquired from 578 patients who underwent prostate biopsy, and compare our model to baseline models and other large-scale studies in the literature. Results and Conclusions: Our model shows consistent and substantial performance improvements compared to ROI-scale-only models. It achieves 80.3% AUROC, a statistically significant improvement over ROI-scale classification. We also compare our method to large studies on prostate cancer detection, using other imaging modalities. Our code is publicly available at www.github.com/med-i-lab/TRUSFormer

翻译：既往大量基于超声的前列腺癌检测机器学习方法，将超声信号中与前列腺活检组织穿刺轨迹（即活检核心）重叠的小区域感兴趣区域（ROI）进行分类。此类ROI尺度模型受限于弱标签问题——因活检核心的组织病理学结果仅能近似反映ROI中癌症的分布情况。此外，ROI尺度模型无法利用病理学家常规评估的上下文信息，即在识别癌症时未考虑周围组织特征及更大尺度的趋势模式。本研究旨在通过多尺度方法（即ROI尺度与活检核心尺度）提升癌症检测性能。方法：我们提出的多尺度方法整合了（i）利用自监督学习训练的"ROI尺度"模型以提取小ROI特征，以及（ii）处理穿刺轨迹区域多个ROI特征集合的"核心尺度"Transformer模型，用于预测对应活检核心的组织类型。作为副产品，注意力图谱可支持在ROI尺度上定位癌症区域。我们使用578例接受前列腺穿刺活检患者的微超声数据集对该方法进行分析，并与基线模型及文献中其他大规模研究进行对比。结果与结论：相较于仅使用ROI尺度的模型，本模型展现出持续且显著的性能提升，AUROC达到80.3%，较ROI尺度分类具有统计学显著改进。我们还将本方法与使用其他影像模态的前列腺癌检测大规模研究进行对比。代码已开源于www.github.com/med-i-lab/TRUSFormer。