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深度学习,放射学和放射基因组学在数字乳腺图片合成中的应用:系统性审查

Sadam Hussain¹, Yareth Lafarga-Osuna², Mansoor Ali²

¹School of Engineering and Sciences, Tecnológico de Monterrey, Ave. Eugenio Garza Sada 2501, 64849, Monterrey, Mexico. a01753094@tec.mx.

BMC bioinformatics

|October 26, 2023

概括

这篇评论探讨了深度学习,放射学和放射基因组学,用于使用数字乳腺突变合成 (DBT) 检测乳腺癌. 它强调了DBT.

科学领域:

医疗成像医学成像
人工智能的人工智能
基因组学就是基因组学.

背景情况:

人工智能和成像科学的进步使得通过放射学和放射基因组学等技术实现了个性化的医疗保健.
数字乳腺断层合成 (DBT) 是一种3D乳房扫描,在乳腺癌诊断方面表现有前途,补充了传统方法.
人工智能算法从DBT的技术进步中受益,用于准确的医学图像解释.

研究的目的:

在乳房图像分析中全面审查深度学习 (DL),放射学和放射基因组学.
专注于DBT,合成乳房镜像 (SM) 和全场数字乳房镜像 (FFDM).
为研究人员提供关于乳房成像中的DL,放射学和放射基因组学的系统知识.

主要方法:

500篇文章的系统文献综述,根据定义的标准选择了30篇文章.
对应用DL,放射学和放射基因组学到DBT图像的研究进行分析.
临床部署和开发的不同模型的基准测试.

主要成果:

确定了30项相关研究,以全面审查乳腺图像分析技术.
在DBT图像上对放射学,放射基因组学和DL模型进行并行比较,提高了临床意识.
该评论作为使用DBT早期发现乳腺癌的指南.

结论:

通过提供DBT中DL,放射学和放射基因组学的综合概述,促进跨学科研究.
向研究人员指导,在人工智能驱动的乳房成像中寻找新的研究方向.
支持开发和临床部署先进的乳腺癌检测模型.

关键词:

乳腺癌是什么? 乳腺癌是什么?深度学习是一种深度学习.数字乳腺图片合成损伤的分类损伤的分类损伤检测检测器可以检测到损伤.医学成像医学成像放射基因组学是指放射基因组学.无线电学 (Radiomics) 是一种辐射学.

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