论文标题
冷冻至par蛋白：借助石蜡和生成对抗网络的组织学冷冻切片分类
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks
论文作者
论文摘要
与石蜡切片相反，在手术干预过程中可以迅速产生冷冻切片。该过程允许外科医生在干预期间等待组织学发现，以基于组织学结果的术中决策。但是，与石蜡切片相比，冷冻切片的质量通常较低，从而导致较高的分类比率更高。在这项工作中，我们研究了该部分类型对甲状腺癌分类的自动决策支持方法的影响。这是通过由单个患者对组成的数据集启用的。此外，我们研究了冷冻到帕脂蛋白翻译是否可以帮助优化分类分数。最后，我们提出了一种特定的数据增强策略，以处理少量培训数据并进一步提高分类准确性。
In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the section type on automated decision support approaches for classification of thyroid cancer. This was enabled by a data set consisting of pairs of sections for individual patients. Moreover, we investigated, whether a frozen-to-paraffin translation could help to optimize classification scores. Finally, we propose a specific data augmentation strategy to deal with a small amount of training data and to increase classification accuracy even further.
