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AUTOMATED DETECTION AND ANNOTATION OF PROSTATE CANCER ON HISTOPATHOLOGY SLIDES

机译:组织学滑片上前列腺癌的自动检测和注释

摘要

Automated, machine learning-based systems are described for the analysis and annotation (i.e., detection or delineation) of prostate cancer (PCa) on histologically-stained pathology slides of prostatectomy specimens. A technical framework is described for automating the annotation of predicted PCa that is based on, for example, automated spatial alignment and colorimetric analysis of both H&E and IHC whole-slide images (WSIs). The WSIs may, as one example, be stained with a particular triple-antibody cocktail against high-molecular weight cytokeratin (HMWCK), p63, and a-methylacyl CoA racemase (AMACR).
机译:描述了一种基于机器学习的自动化系统,用于对前列腺切除术标本的组织学染色病理切片进行前列腺癌(PCa)的分析和注释(即检测或描绘)。描述了一种用于自动化预测PCa注释的技术框架,该技术框架基于,例如,H&E和IHC全幻灯片图像(WSI)的自动空间对齐和比色分析。作为一个实例,WSI可以用针对高分子量细胞角蛋白(HMWCK),p63和α-甲基酰基辅酶A消旋酶(AMACR)的特定三抗体混合物染色。

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