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Journal Abstract Search

762 related items for PubMed ID: 30877461

  • 1. Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.
    Lee JH, Ha EJ, Kim JH.
    Eur Radiol; 2019 Oct; 29(10):5452-5457. PubMed ID: 30877461
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  • 6. [Clinical application of convolutional neural network in pathological diagnosis of metastatic lymph nodes of gastric cancer].
    Wang SZ, Wang JG, Lu Y, Zhang YJ, Xin FJ, Liu SL, Zhang XX, Liu GW, Li S, Sui D, Wang DS.
    Zhonghua Wai Ke Za Zhi; 2019 Dec 01; 57(12):934-938. PubMed ID: 31826599
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  • 9. Deep Learning-Based Computer-Aided Diagnosis System for Localization and Diagnosis of Metastatic Lymph Nodes on Ultrasound: A Pilot Study.
    Lee JH, Baek JH, Kim JH, Shim WH, Chung SR, Choi YJ, Lee JH.
    Thyroid; 2018 Oct 01; 28(10):1332-1338. PubMed ID: 30132411
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  • 10. Artificial intelligence-based prediction of cervical lymph node metastasis in papillary thyroid cancer with CT.
    Wang C, Yu P, Zhang H, Han X, Song Z, Zheng G, Wang G, Zheng H, Mao N, Song X.
    Eur Radiol; 2023 Oct 01; 33(10):6828-6840. PubMed ID: 37178202
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  • 11. Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.
    Zhou LQ, Wu XL, Huang SY, Wu GG, Ye HR, Wei Q, Bao LY, Deng YB, Li XR, Cui XW, Dietrich CF.
    Radiology; 2020 Jan 01; 294(1):19-28. PubMed ID: 31746687
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  • 12. Deep learning nomogram for predicting lymph node metastasis using computed tomography image in cervical cancer.
    Li P, Feng B, Liu Y, Chen Y, Zhou H, Chen Y, Li W, Long W.
    Acta Radiol; 2023 Jan 01; 64(1):360-369. PubMed ID: 34874188
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  • 16. Artificial intelligence for pre-operative lymph node staging in colorectal cancer: a systematic review and meta-analysis.
    Bedrikovetski S, Dudi-Venkata NN, Kroon HM, Seow W, Vather R, Carneiro G, Moore JW, Sammour T.
    BMC Cancer; 2021 Sep 26; 21(1):1058. PubMed ID: 34565338
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  • 17. Deep learning assisted contrast-enhanced CT-based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases.
    Xu X, Xi L, Wei L, Wu L, Xu Y, Liu B, Li B, Liu K, Hou G, Lin H, Shao Z, Su K, Shang Z.
    Eur Radiol; 2023 Jun 26; 33(6):4303-4312. PubMed ID: 36576543
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