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快速细分和诊断乳腺瘤超声波图像在超声波水平使用深度学习

Lei Yang¹, Baichuan Zhang², Fei Ren³

¹Strategic Support Force Medical Center, Beijing 100024, China.

Bioengineering (Basel, Switzerland)

|October 28, 2023

概括

这项研究引入了一种新的AI系统,用于使用超声波图像进行乳腺癌查. 卷积神经网络方法提高了诊断速度和准确性,帮助医生在早期发现乳腺癌.

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:

乳腺癌是妇女死亡的主要原因,需要有效的查方法.
超声波是乳腺癌查的首选工具,但实时诊断是主观的,耗时的.
人工智能协助对于提高诊断准确性和减少错过诊断至关重要,特别是在服务不足的地区.

研究的目的:

开发一种人工智能驱动的系统,用于使用超声波图像对乳腺癌进行辅助诊断.
提高乳腺癌查和早期检测的速度和准确性.

主要方法:

使用卷积神经网络 (CNN) 进行乳房超声波图像分析的两阶段方法.
基于注意力的语义细分用于瘤定位和大小识别.
基于CNN的良性和恶性乳腺结节的分类,使用精心策划的数据集.

主要成果:

在瘤识别方面达到0.89 MIoU,在区分良性和恶性结节方面达到97%的准确性.
人工智能系统的诊断性能与高级临床医生竞争,超过初级医生.
该系统提供快速的初步诊断,简化临床工作流程.

结论:

开发的AI方法显著提高了乳腺癌诊断速度和早期查率.
这种人工智能系统作为超声波乳腺癌诊断的宝贵工具.
这项技术有可能通过更早,更准确的检测来改善患者的治疗结果.

关键词:

辅助诊断辅助诊断是一种辅助诊断.乳腺癌乳腺癌乳腺癌卷积神经网络是一种卷积神经网络.瘤的识别瘤的识别超声波超声波是指超声波的使用.

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