# Deep Learning for Tumor Classification in Imaging Mass Spectrometry

arXiv: https://arxiv.org/abs/1705.01015 · HTML: https://mlsanity.com/paper/1705.01015/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 May 2017.

## Built on by (most cited first, 9 of 9)

- [Combining Image Features and Patient Metadata to Enhance Transfer Learning](https://mlsanity.com/paper/2110.05239/) (2021)
- [Deeply supervised UNet for semantic segmentation to assist dermatopathological assessment of Basal Cell Carcinoma (BCC)](https://mlsanity.com/paper/2103.03759/) (2021)
- [Spatial self-supervised Peak Learning and correlation-based Evaluation of peak picking in Mass Spectrometry Imaging](https://mlsanity.com/paper/2603.10487/) (2026)
- [Transparent Model of Unabridged Data (TMUD)](https://mlsanity.com/paper/2106.07558/) (2021)
- [spectrai: A deep learning framework for spectral data](https://mlsanity.com/paper/2108.07595/) (2021)
- [Enhanced Transfer Learning Through Medical Imaging and Patient Demographic Data Fusion](https://mlsanity.com/paper/2111.14388/) (2021)
- [Deep Learning for Size and Microscope Feature Extraction and Classification in Oral Cancer: Enhanced Convolution Neural Network](https://mlsanity.com/paper/2208.07855/) (2022)
- [Supervised topological data analysis for MALDI mass spectrometry imaging applications](https://mlsanity.com/paper/2302.13948/) (2023)
- [IonMorphNet: Generalizable Learning of Ion Image Morphologies for Peak Picking in Mass Spectrometry Imaging](https://mlsanity.com/paper/2604.19369/) (2026)

## Abstract (arXiv)

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.