Prostate cancer (PCa) is one of the most common and aggressive cancers worldwide. The Gleason score (GS) system is the standard way of classifying prostate cancer and the most reliable method to determine the severity and treatment to follow. The pathologist looks at the arrangement of cancer cells in the prostate and assigns a score on a scale that ranges from 6 to 10. Automatic analysis of prostate whole-slide images (WSIs) is usually addressed as a binary classification problem, which misses the finer distinction between stages given by the GS. This paper presents a probabilistic deep learning ordinal classification method that can estimate the GS from a prostate WSI. Approaching the problem as an ordinal regression task using a differentiable probabilistic model not only improves the interpretability of the results, but also improves the accuracy of the model when compared to conventional deep classification and regression architectures.

中文翻译：

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前列腺组织分级与深量子测量序数回归

前列腺癌（PCa）是全球最常见和侵略性癌症之一。格里森评分（GS）系统是对前列腺癌进行分类的标准方法，也是确定严重程度和后续治疗方法的最可靠方法。病理学家会检查前列腺癌细胞的排列情况，并在6到10的范围内分配分数。通常，对前列腺全玻片（WSI）的自动分析通常被视为二进制分类问题，因而错过了更精细的分类标准。 GS给出的阶段之间的区别。本文提出了一种概率深度学习序数分类方法，该方法可以从前列腺WSI估计GS。使用可微概率模型将问题作为有序回归任务来处理，不仅可以提高结果的可解释性，