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Machine learning based non-invasive diagnosis of thyroid disease

机译:基于机器学习的甲状腺疾病的非侵入性诊断

摘要

A system includes a computing device that receives a query thyroid image, where the query thyroid image is an ultrasound image of a thyroid comprising a thyroid nodule of interest. The computing device processes the query thyroid nodule image using a machine learning model to identify at least one labelled thyroid image from a plurality of labelled thyroid images that is similar to the query thyroid nodule image. The plurality of labelled thyroid images are used as training data to generate the machine learning model. The at least one labelled thyroid image has labels associated therewith and comprises an ultrasound image of a thyroid nodule that has a confirmed diagnosis. The computing device generates an output report based on the labels associated with the at least one labelled thyroid image, where the output report indicates whether the thyroid nodule of interest resembles a malignant thyroid nodule or benign thyroid nodule.
机译:系统包括接收查询甲状腺图像的计算设备,其中查询甲状腺图像是甲状腺的超声图像,其包含感兴趣的甲状腺结节。计算设备使用机器学习模型处理查询甲状腺结节图像,以从类似于查询甲状腺结节图像的多个标记的甲状腺图像鉴定至少一个标记的甲状腺图像。多个标记的甲状腺图像用作训练数据以产生机器学习模型。至少一个标记的甲状腺图像具有与其相关的标记,并且包括具有确认诊断的甲状腺结节的超声图像。计算设备基于与至少一个标记的甲状腺图像相关联的标记产生输出报告,其中输出报告指示感兴趣的甲状腺结节是否类似于恶性甲状腺结节或良性甲状腺结节。

著录项

  • 公开/公告号US10993653B1

    专利类型

  • 公开/公告日2021-05-04

    原文格式PDF

  • 申请/专利权人 JOHNSON THOMAS;

    申请/专利号US201916505622

  • 发明设计人 JOHNSON THOMAS;

    申请日2019-07-08

  • 分类号G06K9;A61B5;A61B8/08;G06T7;

  • 国家 US

  • 入库时间 2022-08-24 18:32:26

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