Cancer detection in mass spectrometry imaging data by recurrent neural networks

F. Ghazvinian Zanjani, A. Panteli, Svitlana Zinger, Fons van der Sommen, Tao Tan, B. Balluff, D.R.N. Vos, S.R. Ellis, R.M.A. Heeren, M. Lucas, H.A. Marquering, I. Jansen, C.D. Savci-Heijink, D.M. de Bruin, P.H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

10 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)978-1-5386-3641-1
Publication statusPublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging (ISBI 2019) - Venice, Italy
Duration: 8 Apr 201911 Apr 2019
Conference number: 16


Conference16th IEEE International Symposium on Biomedical Imaging (ISBI 2019)
Abbreviated titleISBI 2019


  • Cancer detection
  • Deep learning
  • Long short-term memory (lstm)
  • Mass spectrometry imaging (msi)
  • Recurrent neural networks (rnn)


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