Fig. 1.

The pipeline of training and testing the model for diagnosis of thyroid nodules from frozen sections. Module (A) shows how patches of three categories are cropped from annotated slides. Uncertain patches and malignant patches are cropped from uncertain and malignant slides, respectively. Benign patches are cropped from benign regions in each of the three slide types. All cropped patches are used to fine-tune the deep learning model in module (B). In the testing stage, the trained model is applied to all patches inside localized tissues in testing slides. All patch predictions are integrated to form the final diagnosis according to a rule-based protocol. Note: “N” stands for negative, namely benign patch; “U” stands for uncertain patch; and “P” stands for positive, namely malignant patch.