Automatic Detection of Thyroid Nodule Characteristics From 2D Ultrasound Images

Explore the Diagnostic Efficiency of Chinese Thyroid Imaging Reporting and Data Systems by Comparing With the Other Four Systems (ACR TI-RADS, Kwak-TIRADS, KSThR-TIRADS, and EU-TIRADS): A Single-Center Study

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A generic deep learning framework to classify thyroid and breast lesions in ultrasound images

(2020) Yi-Cheng Zhu et al.   ULTRASONICS

A novel TIRADS of US classification

(2018) Yan Zhuang et al.   Biomedical Engineering Online

Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

(2018) Freddie Bray et al.   CA-A CANCER JOURNAL FOR CLINICIANS

Computer-Aided Diagnosis for Breast Ultrasound Using Computerized BI-RADS Features and Machine Learning Methods

(2016) Juan Shan et al.   ULTRASOUND IN MEDICINE AND BIOLOGY

Quantitative analysis of echogenicity for patients with thyroid nodules

(2016) Ming-Hsun Wu et al.   Scientific Reports

Thyroid Imaging Reporting and Data System for US Features of Nodules: A Step in Establishing Better Stratification of Cancer Risk

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An Ultrasonogram Reporting System for Thyroid Nodules Stratifying Cancer Risk for Clinical Management

(2009) Eleonora Horvath et al.   JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM