基于深度学习的超声图像智能甲状腺结节诊断系统

Zhike Yi, A. Hao, Wenfeng Song, Hongyi Li, Bowen Li
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引用次数: 3

摘要

目前,甲状腺癌已成为严重的全球性公共卫生问题,超声是评估甲状腺结节最重要的影像学手段。但甲状腺疾病的超声诊断结果易受医生经验、水平、身份等因素的影响。因此需要智能诊断系统辅助医生进行更加客观的定性和定量分析,减少主观经验对诊断结果的影响。本文提出了一种基于超声图像的甲状腺结节风险评估深度学习算法,并构建了基于该算法的甲状腺超声图像智能诊断系统。该系统作为辅助诊断工具,使用方便,可显著提高甲状腺癌诊断的准确性。为了验证该系统的有效性,我们与北京协和医院合作对该系统进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Intelligent Thyroid Nodule Diagnosis System over Ultrasound Images Based on Deep Learning
At present, thyroid cancer has become a serious global public health problem, and ultrasound is the most important imaging method to assess thyroid nodules. But ultrasound diagnostic results of thyroid disease are susceptible to doctors' experiences, levels, status and other factors. So it needs intelligent diagnostic system to assist the doctors to make more objective qualitative and quantitative analyses, to reduce the impact of subjective experience on the diagnostic results. In this paper, a deep learning algorithm for thyroid nodule risk assessment based on ultrasound images is proposed, and an intelligent diagnostic system of thyroid ultrasound image based on this algorithm is constructed. As an aided diagnostic tool, the system is easy to use and can significantly improve the accuracy for determination of thyroid cancer. To verify the effectiveness of the system, we collaborate with the Peking Union Medical College Hospital to test this system.
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