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:大量先前用于超声波前列腺癌检测的机器学习方法对超声波前列腺癌检测方法进行了大量分类,将引起关注的小区域(ROI)的超声波信号分类为与前列组织组织生物检查(称为生物心理核心)相对应的较大针纹痕迹(ROI)。这些ROI规模模型的标签薄弱,因为生物心理核心的生理病理学结果仅接近于ROI的分布。ROI规模模型没有利用病理学家通常考虑的背景信息,即它们不考虑在确定癌症时对周围组织和较大趋势感兴趣的小区域(ROIs)的信息。我们的目标是通过多尺度(即ROI)和生物心理核心规模(即生物心理核心核心)的追踪来改进癌症检测。我们多尺度方法结合了(i)一种“ROI”规模模型,通过自我监督的学习来提取小肠癌的特征,以及(ii)“核心”变压模型,从针头/直径区域的多个ROI/直径比值区域(我们)的组织类型改进。我们注意地图,通过不断的模型来比较模型,并用模型来分析我们内部实验室的大规模的模型分析我们的数据。</s>