  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. Deep learning
offers an approach to learn feature extraction and classification combined in a
single model. Commonly these steps are handled separately in IMS data analysis,
hence deep learning offers an alternative strategy worthwhile to explore.
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
challenging tumor classification tasks and compared to a baseline approach.
Competitiveness of the proposed methods are 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 task. Thus, this study may serve
as a starting point for further development of deep learning approaches in IMS
classification tasks.
