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基于深度转移学习的方法用于葡萄糖载体-1 (GLUT1) 表达式评估

Maisun Mohamed Al Zorgani¹, Hassan Ugail², Klaus Pors³

¹Faculty of Engineering and Informatics, School of Media, Design and Technology, University of Bradford, Richmond Road, Bradford, BD7 1DP, UK. Maisunmalzorgani@gmail.com.

Journal of digital imaging

|September 5, 2023

概括

在使用深度学习的结直肠癌图像中自动化葡萄糖输送器-1 (GLUT-1) 评分提高了准确性. 这种方法减少了评估瘤缺氧标志物的主观性,提高了临床预测.

科学领域:

在瘤学瘤学.
医疗成像医学成像
计算病理学计算病理学

背景情况:

葡萄糖载体-1 (GLUT-1) 表达是免疫组织化学 (IHC) 图像中瘤缺氧的关键生物标志物.
目前对GLUT-1评分的视觉评估是主观的,导致临床实践中病理学家间的变化.
准确的GLUT-1评分对于预测瘤缺氧标志物至关重要.

研究的目的:

开发和评估一种用于评估IGC结直肠癌图像中的GLUT-1评分的自动化方法.
为了比较GLUT-1评分的各种深度转移学习模型的性能.
建立一个更客观,更可靠的瘤缺氧评估方法.

主要方法:

利用六个预训练的卷积神经网络 (CNN) 架构 (AlexNet,VGG16,GoogleNet,ResNet50,DenseNet-201,ShuffleNet) 进行深度转移学习.
CNNs被微调为分类器或用支持向量机 (SVM) 作为特征提取器.
在IHC图像中评估GLUT-1分数的分类,使用自动特征提取和分类.

主要成果:

性能最好的模型是使用来自DenseNet-201,ResNet50和GoogLeNet的融合深度特征 (Feat-Concat) 的SVM分类器.
该模型实现了98.86%的高预测准确度,超过了其他测试的分类器.
在培训时间和资源方面,现成的特征提取证明比微调更有效.

结论:

使用深度学习的自动 GLUT-1 评分为人工评估提供了一个高度准确和客观的替代方案.
深度特征提取与SVM相结合,为瘤缺氧生物标志物分析提供了强大的方法.
开发的自动化方法有可能提高在临床环境中预测瘤缺氧标志物的可靠性.

关键词:

结肠直肠癌是一种癌症.深度转移学习是指深度转移学习.在GLUT-1中得分.图像分析 IHC 图像分析瘤缺氧是因为瘤缺氧.

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