论文标题
基于深度学习的组织病理学图像中的AGNOR得分自动评估
Deep Learning-Based Automatic Assessment of AgNOR-scores in Histopathology Images
论文作者
论文摘要
核仁组织者区域（NOR）是参与RNA转录的DNA的一部分。由于相关蛋白质的银亲和力，可以使用基于银的染色可视化芳香的NOR（Agnors）。每个核的平均AGNOR数量已被证明是预测许多肿瘤结果的预后因素。由于对Agnor的手动检测很费力，因此自动化具有很高的兴趣。我们提出了一条基于深度学习的管道，用于自动从组织病理学部分确定AGNOR评分。对六位病理学家进行了另外的注释实验，以对我们的方法进行独立的绩效评估。在所有评估者和图像中，我们在专家和模型的高分评分之间发现了平方平方误差为0.054，这表明我们的方法提供了与人类相当的性能。
Nucleolar organizer regions (NORs) are parts of the DNA that are involved in RNA transcription. Due to the silver affinity of associated proteins, argyrophilic NORs (AgNORs) can be visualized using silver-based staining. The average number of AgNORs per nucleus has been shown to be a prognostic factor for predicting the outcome of many tumors. Since manual detection of AgNORs is laborious, automation is of high interest. We present a deep learning-based pipeline for automatically determining the AgNOR-score from histopathological sections. An additional annotation experiment was conducted with six pathologists to provide an independent performance evaluation of our approach. Across all raters and images, we found a mean squared error of 0.054 between the AgNOR- scores of the experts and those of the model, indicating that our approach offers performance comparable to humans.
