对于前列腺癌患者,Gleason评分是最重要的预后因素之一,可能决定独立于分期的治疗。然而,Gleason评分基于肿瘤形态的主观显微镜检查并且具有较差的再现性。在这里,我们提出了一个深度学习系统(DLS),用于Gleason评分前列腺切除术的全幻灯片图像。我们的系统是使用来自1,226张幻灯片的1.12亿个病理学家注释的图像片段开发的,并在331个幻灯片的独立验证数据集上进行评估,其中参考标准由泌尿生殖专家病理学家建立。在验证数据集中,29名一般病理学家的平均准确度为0.61。 DLS的诊断准确率显着提高0.70(p = 0.002),并且与临床随访数据的相关性趋向于更好的患者风险分层。我们的方法可以提高格里森评分的准确性和随后的治疗决策,特别是在专业知识不可用的情况下。 DLS还超越了当前的格里森系统,以更精细地表征和定量肿瘤形态,为格里森系统本身的细化提供了机会。
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The damage personal attacks cause to online discourse motivates manyplatforms to try to curb the phenomenon. However, understanding the prevalenceand impact of personal attacks in online platforms at scale remainssurprisingly difficult. The contribution of this paper is to develop andillustrate a method that combines crowdsourcing and machine learning to analyzepersonal attacks at scale. We show an evaluation method for a classifier interms of the aggregated number of crowd-workers it can approximate. We applyour methodology to English Wikipedia, generating a corpus of over 100k highquality human-labeled comments and 63M machine-labeled ones from a classifierthat is as good as the aggregate of 3 crowd-workers, as measured by the areaunder the ROC curve and Spearman correlation. Using this corpus ofmachine-labeled scores, our methodology allows us to explore some of the openquestions about the nature of online personal attacks. This reveals that themajority of personal attacks on Wikipedia are not the result of a few malicioususers, nor primarily the consequence of allowing anonymous contributions fromunregistered users.
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