Cancer Detection In Mass Spectrometry Imaging Data By Recurrent Neural Networks

F.G. Zanjani, A. Panteli, S. Zinger, Fons van der Sommen, T. Tan, Benjamin Balluff, Naomi Vos, Shane Ellis, Ron Heeren, Marit Lucas, Henk Marquering, I. Jansen, C. D. Savci-Heijink, D.M. de Bruin, P. H. N. de With

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademic

1 Citation (Web of Science)

Abstract

Mass spectrometry imaging (MS1) reveals the localization of a broad scale of compounds ranging from metabolites to proteins in biological tissues. This makes MS1. 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.
Original languageEnglish
Title of host publication2019 IEEE 16TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2019)
PublisherIEEE
Pages674-678
Number of pages5
Volume2019
ISBN (Print)978-1-5386-3641-1
DOIs
Publication statusPublished - 2019

Publication series

SeriesIEEE International Symposium on Biomedical Inmaging Proceedings
ISSN1945-7928

Keywords

  • Recurrent Neural Networks (RNN)
  • Mass Spectrometry Imaging (MSI)
  • cancer detection
  • deep learning
  • long short-term memory (LSTM)
  • CLASSIFICATION

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