Cancer Detection in Mass Spectrometry Imaging Data by Recurrent Neural Networks

Mass spectrometry imaging (MSI) reveals the localization of a broad scale of compounds ranging from metabolites to proteins in biological tissues. This makes MSI an attractive tool in biomedical research for studying diseases. Computer-aided diagnosis (CAD) systems facilitate the analysis of the mol...

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Bibliographic Details
Published in:	2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) pp. 674 - 678
Main Authors:	Zanjani, F. Ghazvinian, Panteli, A., Zinger, S., Sommen, F. van der, Tan, T., Balluff, B., Vos, D. R. N., Ellis, S. R., Heeren, R. M. A., Lucas, M., Marquering, H. A., Jansen, I., Savci-Heijink, C. D., de Bruin, D. M., de With, P. H. N.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-04-2019
Subjects:	Bladder Cancer cancer detection deep learning long short-term memory (LSTM) Lung Mass Spectrometry Imaging (MSI) Merging Recurrent neural networks Recurrent Neural Networks (RNN) Solid modeling Training
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Summary:	Mass spectrometry imaging (MSI) reveals the localization of a broad scale of compounds ranging from metabolites to proteins in biological tissues. This makes MSI an attractive tool in biomedical research for studying diseases. Computer-aided diagnosis (CAD) systems facilitate the analysis of the molecular profile in tumor tissues to provide a distinctive fingerprint for finding biomarkers. In this paper, the performance of recurrent neural networks (RNNs) is studied on MSI data to exploit their learning capabilities for finding irregular patterns and dependencies in sequential data. In order to design a better CAD model for tumor detection/classification, several configurations of Long Short-Time Memory (LSTM) are examined. The proposed model consists of a 2-layer bidirectional LSTM, each containing 100 LSTM units. The proposed RNN model outperforms the state-of-the-art CNN model by 1.87% and 1.45% higher accuracy in mass spectra classification on lung and bladder cancer datasets with a sixfold faster training time.
ISSN:	1945-8452
DOI:	10.1109/ISBI.2019.8759571