Deep learning for tumor classification in imaging mass spectrometry
Deep learning for tumor classification in imaging mass spectrometry
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DOI：
10.1093/bioinformatics/btx724
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发表时间：
2018-04-01
期刊：
影响因子：
5.8
通讯作者：
Maass, Peter
中科院分区：
文献类型：
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作者：
Behrmann, Jens;Etmann, Christian;Maass, Peter
动机：使用成像质谱（IMS）数据的肿瘤分类在病理学的未来应用中具有很高的潜力。由于数据的复杂性和大小，需要自动化特征提取和分类步骤来完全处理数据。由于质谱与图像数据具有一定的结构相似性，深度学习可以为IMS数据的分类提供一种有前途的策略，因为它已成功应用于图像分类。在方法上，我们提出了一种基于深度卷积网络的自适应架构来处理质谱数据的特征，以及基于灵敏度分析在谱域中解释所学习的模型的策略。所提出的方法进行评估，两个算法上具有挑战性的肿瘤分类任务，并比较基线方法。所提出的方法的竞争力显示在这两个任务通过交叉验证研究的性能。此外，所提出的敏感性分析揭示生物学上合理的影响，以及所考虑的任务的混杂因素的学习模型进行了分析。因此，这项研究可以作为IMS分类任务中深度学习方法进一步发展的起点。
Motivation: Tumor classification using imaging mass spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Since mass spectra exhibit certain structural similarities to image data, deep learning may offer a promising strategy for classification of IMS data as it has been successfully applied to image classification.Results: Methodologically, we propose an adapted architecture based on deep convolutional networks to handle the characteristics of mass spectrometry data, as well as a strategy to interpret the learned model in the spectral domain based on a sensitivity analysis. The proposed methods are evaluated on two algorithmically challenging tumor classification tasks and compared to a baseline approach. Competitiveness of the proposed methods is shown on both tasks by studying the performance via cross-validation. Moreover, the learned models are analyzed by the proposed sensitivity analysis revealing biologically plausible effects as well as confounding factors of the considered tasks. Thus, this study may serve as a starting point for further development of deep learning approaches in IMS classification tasks.