Background and objectives: The vast size of the histopathology whole slide image poses formidable challenges to its automatic diagnosis. With the goal of computer-aided diagnosis and the insights that suspicious regions are generally easy to identify in thyroid whole slide images (WSIs), we develop an interactive whole slide diagnostic system for thyroid frozen sections based on the suspicious regions preselected by pathologists.

Methods:We propose to generate feature representations for the suspicious regions via extracting and fusing patch features using deep neural networks. We then evaluate region classification and retrieval on four classifiers and three supervised hashing methods based on the feature representations. The code is released at https://github.com/PingjunChen/ThyroidInteractive.

Results: We evaluate the proposed system on 345 thyroid frozen sections and achieve 96.1% cross-validated classification accuracy, and retrieval mean average precision (MAP) of 0.972.

Conclusions: With the participation of pathologists, the system possesses the following four notable advantages compared to directly handling whole slide images: 1) Reduced interference of irrelevant regions; 2) Alleviated computation and memory cost. 3) Fine-grained and precise suspicious region retrieval. 4) Cooperative relationship between pathologists and the diagnostic system. Additionally, experimental results demonstrate the potential of the proposed system on the practical thyroid frozen section diagnosis.

背景和目的：组织病理学全切片图像的巨大尺寸对其自动诊断提出了巨大的挑战。以计算机辅助诊断为目标，并认识到甲状腺全切片图像（WSI）中可疑区域通常很容易识别，我们根据病理学家预选的可疑区域开发了一种交互式甲状腺冰冻切片全切片诊断系统。

方法：我们建议通过使用深度神经网络提取和融合补丁特征来生成可疑区域的特征表示。然后，我们基于特征表示评估四个分类器和三种监督哈希方法的区域分类和检索。代码发布于https://github.com/PingjunChen/ThyroidInteractive。

结果：我们在 345 个甲状腺冰冻切片上评估了所提出的系统，并实现了 96.1% 的交叉验证分类精度，检索平均精度 (MAP) 为 0.972。

结论：在病理学家的参与下，该系统与直接处理整个切片图像相比具有以下四个显着优势：1）减少不相关区域的干扰；2）减少计算和内存成本。3）细粒度、精准的可疑区域检索。4）病理学家与诊断系统之间的合作关系。此外，实验结果证明了该系统在实用甲状腺冰冻切片诊断中的潜力。