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

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.
LanguageEnglish
Title of host publication2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages674-678
ISBN (Electronic)978-1-5386-3641-1
DOIs
StatePublished - 2019
Event16th IEEE International Symposium on Biomedical Imaging - Venice , Italy
Duration: 8 Apr 201911 Apr 2019

Conference

Conference16th IEEE International Symposium on Biomedical Imaging
Abbreviated titleISBI 2019
CountryItaly
CityVenice
Period8/04/1911/04/19

Fingerprint

Recurrent neural networks
Mass spectrometry
Imaging techniques
Computer aided diagnosis
Data storage equipment
Tumors
Tissue
Biomarkers
Metabolites
Proteins

Cite this

Ghazvinian Zanjani, F., Panteli, A., Zinger, S., van der Sommen, F., Tan, T., Balluff, B., ... de With, P. H. N. (2019). Cancer detection in mass spectrometry imaging data by recurrent neural networks. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) (pp. 674-678). [8759571] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ISBI.2019.8759571
Ghazvinian Zanjani, F. ; Panteli, A. ; Zinger, Svitlana ; van der Sommen, Fons ; Tan, Tao ; 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./ Cancer detection in mass spectrometry imaging data by recurrent neural networks. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Piscataway : Institute of Electrical and Electronics Engineers, 2019. pp. 674-678
@inproceedings{609767dc789a4005a074c4afcc2ac465,
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.",
author = "{Ghazvinian Zanjani}, F. and A. Panteli and Svitlana Zinger and {van der Sommen}, Fons and Tao Tan and B. Balluff and D.R.N. Vos and S.R. Ellis and R.M.A. Heeren and M. Lucas and H.A. Marquering and I. Jansen and C.D. Savci-Heijink and {de Bruin}, D.M. and {de With}, P.H.N.",
year = "2019",
doi = "10.1109/ISBI.2019.8759571",
language = "English",
pages = "674--678",
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Ghazvinian Zanjani, F, Panteli, A, Zinger, S, van der Sommen, F, 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 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)., 8759571, Institute of Electrical and Electronics Engineers, Piscataway, pp. 674-678, 16th IEEE International Symposium on Biomedical Imaging, Venice , Italy, 8/04/19. DOI: 10.1109/ISBI.2019.8759571

Cancer detection in mass spectrometry imaging data by recurrent neural networks. / Ghazvinian Zanjani, F.; Panteli, A.; Zinger, Svitlana; van der Sommen, Fons; Tan, Tao; 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 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Piscataway : Institute of Electrical and Electronics Engineers, 2019. p. 674-678 8759571.

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

TY - GEN

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

AU - Ghazvinian Zanjani,F.

AU - Panteli,A.

AU - Zinger,Svitlana

AU - van der Sommen,Fons

AU - Tan,Tao

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.

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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.

U2 - 10.1109/ISBI.2019.8759571

DO - 10.1109/ISBI.2019.8759571

M3 - Conference contribution

SP - 674

EP - 678

BT - 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

ER -

Ghazvinian Zanjani F, Panteli A, Zinger S, van der Sommen F, Tan T, Balluff B et al. Cancer detection in mass spectrometry imaging data by recurrent neural networks. In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). Piscataway: Institute of Electrical and Electronics Engineers. 2019. p. 674-678. 8759571. Available from, DOI: 10.1109/ISBI.2019.8759571