Cancer detection in mass spectrometry imaging data by recurrent neural networks

F. Ghazvinian Zanjani; A. Panteli; S. Zinger; F. Van Der Sommen; T. 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

doi:10.1109/ISBI.2019.8759571

Cancer detection in mass spectrometry imaging data by recurrent neural networks

F. Ghazvinian Zanjani, A. Panteli, S. Zinger, F. Van Der Sommen, T. 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 proceeding › Conference contribution › peer-review

10 Citations (Scopus)

Abstract

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 language	English
Title of host publication	ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
Pages	674-678
Number of pages	5
ISBN (Electronic)	9781538636411
DOIs	https://doi.org/10.1109/ISBI.2019.8759571
Publication status	Published - Apr 2019
Externally published	Yes
Event	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy Duration: 8 Apr 2019 → 11 Apr 2019

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2019-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/Territory	Italy
City	Venice
Period	8/04/19 → 11/04/19

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ISBI.2019.8759571

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Zanjani, F. G., Panteli, A., Zinger, S., Sommen, F. V. D., 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. (2019). Cancer detection in mass spectrometry imaging data by recurrent neural networks. In ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging (pp. 674-678). Article 8759571 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April). IEEE Computer Society. https://doi.org/10.1109/ISBI.2019.8759571

@inproceedings{df24a81e623547caaabe938219f4c3d6,

title = "Cancer detection in mass spectrometry imaging data by recurrent neural networks",

abstract = "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.",

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

author = "Zanjani, {F. Ghazvinian} and A. Panteli and S. Zinger and Sommen, {F. Van Der} and T. Tan and B. Balluff and Vos, {D. R.N.} and Ellis, {S. R.} and Heeren, {R. M.A.} and M. Lucas and Marquering, {H. A.} and I. Jansen and Savci-Heijink, {C. D.} and {De Bruin}, {D. M.} and {De With}, {P. H.N.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",

year = "2019",

month = apr,

doi = "10.1109/ISBI.2019.8759571",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

publisher = "IEEE Computer Society",

pages = "674--678",

booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",

address = "United States",

}

Zanjani, FG, Panteli, A, Zinger, S, Sommen, FVD, Tan, T, Balluff, B, Vos, DRN, Ellis, SR, Heeren, RMA, Lucas, M, Marquering, HA, Jansen, I, Savci-Heijink, CD, De Bruin, DM & De With, PHN 2019, Cancer detection in mass spectrometry imaging data by recurrent neural networks. in ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging., 8759571, Proceedings - International Symposium on Biomedical Imaging, vol. 2019-April, IEEE Computer Society, pp. 674-678, 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019, Venice, Italy, 8/04/19. https://doi.org/10.1109/ISBI.2019.8759571

Cancer detection in mass spectrometry imaging data by recurrent neural networks. / Zanjani, F. Ghazvinian; Panteli, A.; Zinger, S. et al.
ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2019. p. 674-678 8759571 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2019-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Cancer detection in mass spectrometry imaging data by recurrent neural networks

AU - Zanjani, F. Ghazvinian

AU - Panteli, A.

AU - Zinger, S.

AU - Sommen, F. Van Der

AU - Tan, T.

AU - Balluff, B.

AU - Vos, D. R.N.

AU - Ellis, S. R.

AU - Heeren, R. M.A.

AU - Lucas, M.

AU - Marquering, H. A.

AU - Jansen, I.

AU - Savci-Heijink, C. D.

AU - De Bruin, D. M.

AU - De With, P. H.N.

PY - 2019/4

Y1 - 2019/4

N2 - 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.

AB - 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.

KW - Cancer detection

KW - Deep learning

KW - Long short-term memory (lstm)

KW - Mass spectrometry imaging (msi)

KW - Recurrent neural networks (rnn)

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U2 - 10.1109/ISBI.2019.8759571

DO - 10.1109/ISBI.2019.8759571

M3 - Conference contribution

AN - SCOPUS:85073899951

T3 - Proceedings - International Symposium on Biomedical Imaging

SP - 674

EP - 678

BT - ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging

PB - IEEE Computer Society

T2 - 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019

Y2 - 8 April 2019 through 11 April 2019

ER -

Cancer detection in mass spectrometry imaging data by recurrent neural networks

Abstract

Publication series

Conference

Keywords

UN SDGs

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