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知识张力辅助乳房超声波图像助理推理框架

Guanghui Li^1,2, Lingli Xiao³, Guanying Wang⁴

¹School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China.

Healthcare (Basel, Switzerland)

|July 29, 2023

概括

一个新的知识张量模型通过使用高级医生BI-RADS得分来帮助乳房超声诊断. 这种人工智能工具提高了区分良性和恶性乳腺瘤的准确性,提高了早期癌症检测.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

乳腺癌是女性癌症的主要原因,需要早期检测以改善患者的治疗结果.
乳房超声波 (BUS) 对于早期查至关重要,但其准确性取决于医生的专业知识.
诊断解释的变化可能会影响患者的预后.

研究的目的:

开发基于知识张量模型的模型,以协助乳房超声波诊断.
利用高级医生专业知识 (BI-RADS分数) 作为训练模型的黄金标准.
为了提高区分良性和恶性乳腺瘤的准确性.

主要方法:

使用知识张量方法构建了一个通用推理模型.
该模型结合了高级放射科医生的乳腺成像报告和数据系统 (BI-RADS) 评分.
诊断结果与初级医生的诊断结果进行了比较,应用了知识张量融合.

主要成果:

在高级放射科医生数据上训练的知识张量实现了乳腺癌的高诊断AUC0.983.
在初级放射科医生数据上训练的张量显示较低的AUC为0.849.
知识张量融合将整体AUC提高到0.887,减少了错误分类.

结论:

提出的知识张量模型有效地帮助乳房超声波诊断.
该模型有助于减轻高级放射科医生的诊断错误,提高准确性.
这种方法显示出改善乳腺癌查和诊断的巨大潜力.

关键词:

双轮车是双轮车的一种.一般化推论推论的推论.知识张量器知识张量器

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