Deep learning-based analysis of high-frequency, high-resolution micro-ultrasound data shows great promise for prostate cancer detection. Previous approaches to analysis of ultrasound data largely follow a supervised learning paradigm. Ground truth labels for ultrasound images used for training deep networks often include coarse annotations generated from the histopathological analysis of tissue samples obtained via biopsy. This creates inherent limitations on the availability and quality of labeled data, posing major challenges to the success of supervised learning methods. On the other hand, unlabeled prostate ultrasound data are more abundant. In this work, we successfully apply self-supervised representation learning to micro-ultrasound data. Using ultrasound data from 1028 biopsy cores of 391 subjects obtained in two clinical centres, we demonstrate that feature representations learnt with this method can be used to classify cancer from non-cancer tissue, obtaining an AUROC score of 91% on an independent test set. To the best of our knowledge, this is the first successful end-to-end self-supervised learning approach for prostate cancer detection using ultrasound data. Our method outperforms baseline supervised learning approaches, generalizes well between different data centers, and scale well in performance as more unlabeled data are added, making it a promising approach for future research using large volumes of unlabeled data.
翻译:对高频、高分辨率微超声波数据进行深入的学习分析显示,对前列腺癌检测有很大的希望。以前对超声波数据的分析方法基本上遵循受监督的学习范式。用于培训深层网络的超声波图像地面标签往往包括通过生物检查获得的组织样本的生理病理分析产生的粗劣的注释。这对标签数据的供应和质量造成固有的限制,对受监督的学习方法的成功构成重大挑战。另一方面,未贴标签的前列腺超声波数据则更为丰富。在这项工作中,我们成功地将自我监督的代言学习方法应用到微声波数据中。在两个临床中心获得的391个主题的1028个生物心理核心的超声波数据中,我们证明,用这种方法学习的特征表可用于将癌症从非癌症组织分类,在独立测试集中获得91%的AUROC分。而我们最了解的是,这是第一个成功的端到端自我监督的超声波超声波检测方法。在超声波数据中,我们的方法是利用超声波波数据中心进行有希望的大规模数据研究,我们的方法将未来数据库中的一项基础学习。