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Updated: Jun 2, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

DIFLF:一个域不变的特征学习框架,用于单源域概括在乳腺造影分类中的域.

Wanfang Xie¹, Zhenyu Liu², Litao Zhao¹

¹School of Engineering Medicine, Beihang University, Beijing 100191, PR China; Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology of the People's Republic of China, Beijing 100191, PR China.

Computer methods and programs in biomedicine

|January 15, 2025

概括

这项研究引入了一个新的框架,用于单源域概括 (SSDG) 在深度学习模型中用于乳腺癌查. 拟议的方法有效地减少了域位的转移,改善了对未见数据集的模型性能.

关键词:

乳腺癌是什么? 乳腺癌是什么?内容式解模块的内容样式解模块深度学习是一种深度学习.域名通用化域名通用化乳房影像,风格增大模块

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相关实验视频

Last Updated: Jun 2, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Published on: August 16, 2020

Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Published on: November 19, 2018

科学领域:

人工智能的人工智能
医学成像分析医学成像分析
机器学习用于医疗保健

背景情况:

对于乳腺癌查的深度学习 (DL) 模型面临的挑战是,由于领域的转变,它在不同机构中普遍化.
单源域泛化 (SSDG) 旨在在一个数据集上训练模型,并将其应用于多个未见的数据集.
在临床DL应用中,仅使用一个源数据集来缓解域转移是一个重大挑战.

研究的目的:

为单源域泛化提出一个域不变特征学习框架 (DIFLF).
提高DL模型在乳腺癌查中的稳定性和通用性.
为了减少领域转移对乳腺造影分类任务的影响.

主要方法:

开发了一个域不变特征学习框架 (DIFLF),包括一个风格增强模块 (SAM) 和一个内容风格解模块 (CSDM).
SAM利用颜色变换来增加功能多样性并减少模型过拟合.
CSDM使用特征解单元来提取域不变的内容特征,最大限度地减少域特定风格的影响.

主要成果:

在不同特征分布的未见数据集 (PRI2,INbreast,MIAS) 上,DIFLF在分类乳房影像方面表现出色.
获得了高精度和AUC分数,包括PRI2上的0.917精度和0.928AUC,INbreast上的0.882精度和0.893AUC,MIAS上的0.767精度和0.710AUC.
该框架有效地减轻了域名转移的影响,即使特征分布存在显著差异.

结论:

拟议的DIFLF有效地减轻了使用单一源数据集的域名转移.
DIFLF在未见的数据集上实现了出色的乳房镜分类性能,包括具有显著特征分布差异的数据集.
该框架显示了在临床乳腺癌查中强大的和可泛化的DL应用的前景.