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通过磁共振成像检测乳腺癌的深度学习应用:文献综述

Richard Adam¹, Kevin Dell'Aquila¹, Laura Hodges¹

¹Department of Radiology, Albert Einstein College of Medicine and the Montefiore Medical Center, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.

Breast cancer research : BCR

|July 24, 2023

概括

深度学习显示了通过MRI扫描改善乳腺癌检测的希望. 本综述分析了当前的研究,强调了临床应用的挑战和未来方向.

科学领域:

放射学放射学是一门学科.
医疗成像医学成像
人工智能的人工智能

背景情况:

放射性图像分析中的深度学习 (DL) 提供了增强乳腺癌诊断的潜力.
准确的乳腺癌检测对于改善患者的治疗结果至关重要.
磁共振成像 (MRI) 是乳腺癌评估的一个关键方式.

研究的目的:

系统地审查关于深度学习的文献,以使用MRI检测乳腺癌.
在已发表的研究中分析DL算法,方法和性能指标.
确定临床实施的挑战和未来研究途径.

主要方法:

从2015年到2022年12月在多个数据库 (PubMed,语义学者,ACM数字图书馆,谷歌学者,预印库) 进行系统的文献搜索.
包括专注于深度学习的研究,用MRI检测乳腺癌;排除像纹理分析这样的非DL方法.
遵守PRISMA系统审查报告指南.

主要成果:

分析各种深度学习算法,实验设计,MRI序列和基本真相类型.
基于样本大小,病变数量 (良性/恶性) 和报告的准确度指标的研究性能评估.
关于乳腺MRI分析中DL的有效性和局限性的发现综合.

结论:

深度学习具有显著的潜力,可以通过MRI提高乳腺癌检测的诊断准确性.
在广泛的临床部署方面仍然存在挑战,需要进一步的研究和验证.
未来的研究应该专注于解决这些挑战,以促进其融入临床实践.

关键词:

人工智能的人工智能是人工智能.卷积神经网络是一种卷积神经网络.动态对比增强动态对比增强这就是为什么MRI是MRI.机器学习是机器学习.纹理特征分析纹理特征分析

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