使用卷积神经网络的迁移学习对Eu-TIRADS分类的甲状腺结节分层

E. Fartushnyi, Yulia P. Sytch, I. Fartushnyi, K. Koshechkin, G. Lebedev
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引用次数: 1

摘要

本文描述了一种基于超声诊断图像的人工智能系统,根据欧洲甲状腺成像和报告数据系统(Eu-TIRADS)量表评估甲状腺结节恶性潜能及其分层的方法。该方法基于对卷积神经网络的多参数模型及其后续微调使用迁移学习技术。结果表明,即使在由5个Eu-TIRADS类别分类的1129张甲状腺超声图像组成的小数据集上,该方法的应用也提供了很高的训练精度(准确率:0.8,AUC: 0.92)。这使得在临床实践中引入和使用该技术成为可能,作为一种额外的工具(“第二意见”),用于客观评估甲状腺结节恶性肿瘤的风险,以便进一步选择细针活检。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Stratification of thyroid nodules by Eu-TIRADS categories using transfer learning of convolutional neural networks
The article describes a method for assessing the malignancy potential of thyroid nodules and their stratification according to the European Thyroid Imaging And Reporting Data System (Eu-TIRADS) scale based on ultrasound diagnostic images using an artificial intelligence system. The method is based on the use of transfer learning technology for multi-parameter models of convolutional neural networks and their subsequent fine tuning. It was shown that even on a small dataset consisting of 1129 thyroid ultrasound images classified by 5 Eu-TIRADS categories, the application of the method provides high training accuracy (Accuracy: 0.8, AUC: 0.92). This makes it possible to introduce and use this technology in clinical practice as an additional tool (‘second opinion’) for an objective assessment of the risk of malignancy in thyroid nodules for the purpose of their further selection for fine needle biopsy.
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