Center for Industrial Mathematics, University of Bremen, 28359 Bremen, Germany.
SCiLS, 28359 Bremen, Germany.
Bioinformatics. 2018 Apr 1;34(7):1215-1223. doi: 10.1093/bioinformatics/btx724.
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.
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.
https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS.
jbehrmann@uni-bremen.de or christianetmann@uni-bremen.de.
Supplementary data are available at Bioinformatics online.
使用成像质谱（IMS）数据进行肿瘤分类在病理学中有很高的应用潜力。由于数据的复杂性和规模，需要自动化的特征提取和分类步骤来充分处理数据。由于质谱在结构上与图像数据具有某些相似性，因此深度学习可能是一种很有前途的 IMS 数据分类策略，因为它已经成功应用于图像分类。
从方法学角度来看，我们提出了一种基于深度卷积网络的自适应架构，以处理质谱数据的特征，以及一种基于敏感性分析在谱域中解释学习模型的策略。在所提出的方法中，我们评估了两个在算法上具有挑战性的肿瘤分类任务，并与基线方法进行了比较。通过交叉验证研究性能，证明了所提出的方法在两个任务上的竞争力。此外，通过所提出的敏感性分析对学习模型进行分析，揭示了所考虑任务的生物学上合理的影响和混杂因素。因此，这项研究可以作为进一步开发 IMS 分类任务中深度学习方法的起点。
https://gitlab.informatik.uni-bremen.de/digipath/Deep_Learning_for_Tumor_Classification_in_IMS。
jbehrmann@uni-bremen.de 或 christianetmann@uni-bremen.de。
补充数据可在生物信息学在线获得。
