Grading for cancer, based upon the degree of cancer differentiation, plays a major role in describing the characteristics and behavior of the cancer and determining treatment plan for patients. The grade is determined by a subjective and qualitative assessment of tissues under microscope, which suffers from high inter- and intra-observer variability among pathologists. Digital pathology offers an alternative means to automate the procedure as well as to improve the accuracy and robustness of cancer grading. However, most of such methods tend to mimic or reproduce cancer grade determined by human experts. Herein, we propose an alternative, quantitative means of assessing and characterizing cancers in an unsupervised manner. The proposed method utilizes conditional generative adversarial networks to characterize tissues. The proposed method is evaluated using whole slide images (WSIs) and tissue microarrays (TMAs) of colorectal cancer specimens. The results suggest that the proposed method holds a potential for quantifying cancer characteristics and improving cancer pathology.

中文翻译：

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通过条件生成对抗网络进行无监督肿瘤表征

根据癌症分化程度对癌症进行分级，在描述癌症的特征和行为以及确定患者的治疗计划方面起着重要作用。等级由显微镜下组织的主观和定性评估确定，病理学家之间存在高度的观察者间和观察者内变异性。数字病理学提供了一种替代方法来自动化程序以及提高癌症分级的准确性和稳健性。然而，大多数此类方法倾向于模仿或再现人类专家确定的癌症等级。在此，我们提出了一种以无监督方式评估和表征癌症的替代定量方法。所提出的方法利用条件生成对抗网络来表征组织。使用结直肠癌标本的全载玻片图像 (WSI) 和组织微阵列 (TMA) 评估所提出的方法。结果表明，所提出的方法具有量化癌症特征和改善癌症病理学的潜力。