Computer-Assisted Fine-Needle Aspiration Cytology of Thyroid Using Two-Stage Refined Convolutional Neural Network

Fine-needle aspiration cytology (FNAC) is regarded as one of the most important preoperative diagnostic tests for thyroid nodules. However, the traditional diagnostic process of FNAC is time-consuming, and its accuracy is highly related to the experience of the cytopathologist. Computer-aided diagnostic (CAD) systems are rapidly evolving to provide objective diagnostic recommendations. So far, most studies have used fixed-size patches and usually hand-select patches for model training. In this study, we develop a CAD system to address these challenges. In order to be consistent with the diagnostic working mode of cytopathologists, the system is mainly composed of two task modules: the detecting module that is responsible for detecting the regions of interest (ROIs) from the whole slide image of the FNAC, and the classification module that identifies ROIs having positive lesions. The system can then output the top-k ROIs with the highest positive probabilities for the cytopathologists to review. In order to obtain the overall good performance of the system, we compared different object detection and classification models, and used a combination of the YOLOV4 and EfficientNet networks in our system.