An ultrasonography of thyroid nodules dataset with pathological diagnosis annotation for deep learning
Xiaowen Hou, Menglei Hua, Wei Zhang, Jianxin Ji, Xuan Zhang, Huiru Jiang, Mengyun Li, Xiaoxiao Wu, Wenwen Zhao, Shuxin Sun, Lei Cao, L. Wang
Abstract
Ultrasonography (US) of thyroid nodules is often time consuming and may be inconsistent between observers, with a low positivity rate for malignancy in biopsies. Even after determining the ultrasound Thyroid Imaging Reporting and Data System (TIRADS) stage, Fine needle aspiration biopsy (FNAB) is still required to obtain a definitive diagnosis. Although various deep learning methods were developed in medical field, they tend to be trained using TI-RADS reports as image labels. Here, we present a large US dataset with pathological diagnosis annotation for each case, designed for developing deep learning algorithms to directly infer histological status from thyroid ultrasound images. The dataset was collected from two retrospective cohorts, which consists of 8508 US images from 842 cases. Additionally, we explained three deep learning models used as validation examples using this dataset.