과제정보
연구 과제 주관 기관 : Ajou University School of Medicine
참고문헌
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Acharya UR, Faust O, Sree SV, Molinari F, Garberoglio R, Suri JS. Cost-effective and non-invasive automated benign and malignant thyroid lesion classification in 3D contrastenhanced ultrasound using combination of wavelets and textures: a class of
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Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of
$ThyroScan^{TM}$ systems. Ultrasonics 2012;52:508-520 https://doi.org/10.1016/j.ultras.2011.11.003 - Choi YJ, Baek JH, Park HS, Shim WH, Kim TY, Shong YK, et al. A computer-aided diagnosis system using artificial intelligence for the diagnosis and characterization of thyroid nodules on ultrasound: initial clinical assessment. Thyroid 2017;27:546-552 https://doi.org/10.1089/thy.2016.0372
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피인용 문헌
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- The Diagnostic Efficiency of Ultrasound Computer-Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis vol.11, pp.11, 2019, https://doi.org/10.3390/cancers11111759
- Automatic thyroid nodule recognition and diagnosis in ultrasound imaging with the YOLOv2 neural network vol.17, pp.None, 2018, https://doi.org/10.1186/s12957-019-1558-z
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- Computer-Aided Diagnosis Systems in Diagnosing Malignant Thyroid Nodules on Ultrasonography: A Systematic Review and Meta-Analysis vol.9, pp.4, 2018, https://doi.org/10.1159/000504390
- Ultrasound Computer-Aided Diagnosis (CAD) Based on the Thyroid Imaging Reporting and Data System (TI-RADS) to Distinguish Benign from Malignant Thyroid Nodules and the Diagnostic Performance of Radiol vol.26, pp.None, 2018, https://doi.org/10.12659/msm.918452
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- A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience vol.10, pp.None, 2018, https://doi.org/10.3389/fonc.2020.557169
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- Nodular Thyroid Disease in the Era of Precision Medicine vol.10, pp.None, 2018, https://doi.org/10.3389/fendo.2019.00907
- Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training vol.30, pp.6, 2018, https://doi.org/10.1007/s00330-019-06652-4
- False-Positive Malignant Diagnosis of Nodule Mimicking Lesions by Computer-Aided Thyroid Nodule Analysis in Clinical Ultrasonography Practice vol.10, pp.6, 2018, https://doi.org/10.3390/diagnostics10060378
- Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners vol.23, pp.2, 2020, https://doi.org/10.1007/s40477-020-00453-y
- Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images vol.47, pp.9, 2018, https://doi.org/10.1002/mp.14301
- Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review vol.23, pp.4, 2018, https://doi.org/10.2196/25759
- Applications of machine learning and deep learning to thyroid imaging: where do we stand? vol.40, pp.1, 2021, https://doi.org/10.14366/usg.20068
- Artificial intelligence for ultrasonography: unique opportunities and challenges vol.40, pp.1, 2018, https://doi.org/10.14366/usg.20078
- A Computer-Aided Diagnosis System and Thyroid Imaging Reporting and Data System for Dual Validation of Ultrasound-Guided Fine-Needle Aspiration of Indeterminate Thyroid Nodules vol.11, pp.None, 2018, https://doi.org/10.3389/fonc.2021.611436
- Computer-Aided Diagnostic System for Thyroid Nodules on Ultrasonography: Diagnostic Performance Based on the Thyroid Imaging Reporting and Data System Classification and Dichotomous Outcomes vol.42, pp.3, 2018, https://doi.org/10.3174/ajnr.a6922
- 미만성 갑상샘 질환에서 GLCM을 이용한 초음파 영상 분석 vol.15, pp.4, 2021, https://doi.org/10.7742/jksr.2021.15.4.473
- Artificial Intelligence in Thyroid Field-A Comprehensive Review vol.13, pp.19, 2018, https://doi.org/10.3390/cancers13194740