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基于胸部X射线图像的疾病诊断的跨模式深度度度度学习模型

Yufei Jin^1,2, Huijuan Lu^1,2, Zhao Li³

¹China JiLiang University, Hangzhou, 310018 Zhejiang China.

Multimedia tools and applications

|June 26, 2023

概括

这项研究介绍了一种新的跨模态深度度度度学习通用零射击学习 (CM-DML-GZSL) 模型用于医疗图像分析. 该模型使用新的加权损失函数提高了诊断未见疾病的准确性,增强了零射击多标签学习.

科学领域:

人工智能的人工智能
医疗成像医学成像
机器学习机器学习

背景情况:

新出现的疾病通常存在于有限的或没有可用样本进行分析.
零射击学习 (ZSL) 和少射击学习 (FSL) 在医学图像分析中显示出潜力,特别是在罕见或未知的疾病中.
准确的医学图像分类对于及时诊断和治疗至关重要.

研究的目的:

为医疗图像分析提出一种新的跨模态深度度度度学习通用零射击学习 (CM-DML-GZSL) 模型.
在零射击学习模型的培训过程中,解决多标签环境中正负不平衡的挑战.
通过改善视觉特征和语义向量之间的对应性来提高诊断看不见的疾病的准确性.

主要方法:

开发了一个双流网络,包括一个视觉特征提取器,一个固定的语义特征提取器和一个深度回归模块.
引入了一种新的加权聚焦的欧几里德距离度量损失函数,以动态调整样本重量.
实施了一般化的零射击学习方法,用于医学成像中的多标签分类.

主要成果:

拟议的CM-DML-GZSL模型有效地减轻了预测未见类的偏差.
权重聚焦的欧几里德距离度量损失函数成功处理正负不平衡,提高准确性.
对大型公共数据集的实验结果表明,该模型在零射击多标签学习中对胸部X射线诊断的有效性.

结论:

CM-DML-GZSL模型在数据有限的情况下为医疗图像分析提供了一个有前途的解决方案.
新的加权损失函数显著提高了在多标签医疗图像分类中通用零射击学习的性能.
这种方法促进了胸部X射线诊断的零射击多标签学习,为改善诊断能力铺平了道路.

关键词:

胸部X射线胸部X射线跨模式的交叉方式.深度度指标学习 (deep metric learning) 是一种深度度指标学习.一般化的零射击学习.多个标签分类的分类.

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