Interactive thyroid whole slide image diagnostic system using deep representation.

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.