# Deep Learning for Tumor Classification in Imaging Mass Spectrometry

## TL;DR

This paper introduces a deep convolutional network architecture tailored for tumor classification in Imaging Mass Spectrometry data, demonstrating improved performance and interpretability over baseline methods.

## Contribution

It proposes a novel deep learning architecture adapted for IMS data and a spectral sensitivity analysis for model interpretation, advancing automated tumor classification.

## Key findings

- Competitive classification performance on two tumor datasets
- Model interpretability reveals biologically plausible spectral effects
- Sensitivity analysis identifies confounding factors

## Abstract

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.