Generating a multimodal artificial intelligence model to differentiate benign and malignant follicular neoplasms of the thyroid: A proof-of-concept study

Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images

(2022) Yi Tao et al.   Frontiers in Oncology

Multimodal machine learning in precision health: A scoping review

(2022) Adrienne Kline et al.   npj Digital Medicine

Magnetic resonance radiomics features and prognosticators in different molecular subtypes of pediatric Medulloblastoma

(2021) Feng-Chi Chang et al.   PLoS One

Risk Stratifying Indeterminate Thyroid Nodules With Machine Learning

(2021) George Luong et al.   JOURNAL OF SURGICAL RESEARCH

The American Association of Endocrine Surgeons Guidelines for the Definitive Surgical Management of Thyroid Disease in Adults

(2020) Kepal N. Patel et al.   ANNALS OF SURGERY

Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer

(2020) Xing Tang et al.   Biomedical Engineering Online

Application of machine learning to ultrasound images to differentiate follicular neoplasms of the thyroid gland

(2020) Ilah Shin et al.   Ultrasonography

Trends in Thyroid Cancer Incidence and Mortality in the United States, 1974-2013

(2017) Hyeyeun Lim et al.   JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION

Machine Learning for Medical Imaging

(2017) Bradley J. Erickson et al.   RADIOGRAPHICS

Radiomics: Images Are More than Pictures, They Are Data

(2016) Robert J. Gillies et al.   RADIOLOGY

Thyroid Follicular Carcinoma: Sonographic Features of 50 Cases

(2009) John C. Sillery et al.   AMERICAN JOURNAL OF ROENTGENOLOGY

Very high prevalence of thyroid nodules detected by high frequency (13 MHz) ultrasound examination

(2009) S. Guth et al.   EUROPEAN JOURNAL OF CLINICAL INVESTIGATION