The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to accurately identify necrotic regions on hematoxylin and eosin (HE)stained slides and to calculate the ratio of necrosis with minimal annotations on the images. An adaptive method named Learning from Label Fuzzy Proportions (LLFP) was introduced to histopathological image analysis. Two datasets of liver cancer HE slides were collected to verify the feasibility of the method by training on the internal set using cross validation and performing validation on the external set, along with ensemble learning to improve performance. The models from cross validation performed relatively stably in identifying necrosis, with a Concordance Index of the Slide Necrosis Score (CISNS) of 0.91650.0089 in the internal test set. The integration model improved the CISNS to 0.9341 and achieved a CISNS of 0.8278 on the external set. There were significant differences in survival (p=0.0060) between the three groups divided according to the calculated necrosis ratio. The proposed method can build an integration model good at distinguishing necrosis and capable of clinical assistance as an automatic tool to stratify patients with different risks or as a cluster tool for the quantification of histopathological features. We presented a method effective for identifying histopathological features and suggested that the extent of necrosis, especially micronecrosis, in liver cancer is related to patient outcomes.

中文翻译：

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通过从标记模糊比例进行深度学习来量化肿瘤病理微坏死的方法。

在许多癌症中，坏死的存在与肿瘤的进展和患者的预后有关，但是现有的分析很少采用定量方法，因为对组织病理学特征进行人工定量过于昂贵。我们旨在准确地识别苏木精和曙红（HE）染色的幻灯片上的坏死区域，并以最少的图像注释来计算坏死的比率。一种名为“从标签模糊比例学习”（LLFP）的自适应方法被引入到组织病理学图像分析中。收集了两个肝癌HE幻灯片数据集，以通过使用交叉验证在内部集上进行训练并在外部集上进行验证以及集成学习以提高性能来验证该方法的可行性。交叉验证的模型在识别坏死方面相对稳定，内部测试集中的滑动坏死评分（CISNS）一致性指数为0.91650.0089。集成模型将CISNS提高到0.9341，并在外部设备上实现了0.8278的CISNS。根据计算的坏死率划分，三组之间的生存率存在显着差异（p = 0.0060）。所提出的方法可以建立一个能够很好地区分坏死的整合模型，并可以作为对不同风险患者进行分层的自动工具，或者作为对组织病理学特征进行量化的聚类工具，可以提供临床帮助。我们提出了一种可有效识别组织病理学特征的方法，并建议坏死的程度，尤其是微坏死，