Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks

Jannis van Kersbergen, F. Ghazvinian Zanjani, Svitlana Zinger, Fons van der Sommen, Benjamin Balluff, D.R.N. Vos, S.R. Ellis, Ron M.A. Heeren, M. Lucas, H.A. Marquering, I. Jansen, Cemile Dilara Savci-Heijink, D.M. de Bruin, P.H.N. de With

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

Abstract

Imaging mass spectrometry (IMS) is a novel molecular imaging technique to investigate how molecules are distributed between tumors and within tumor region in order to shed light into tumor biology or find potential biomarkers. Convolutional neural networks (CNNs) have proven to be very potent classifiers often outperforming other machine learning algorithms, especially in computational pathology. To overcome the challenge of complexity and high-dimensionality of the IMS data, the proposed CNNs are either very deep or use large kernels, which results in large amount of parameters and therefore a high computational complexity. An alternative is down-sampling the data, which inherently leads to a loss of information. In this paper, we propose using dilated CNNs as a possible solution to this challenge, since it allows for an increase of the receptive field size, neither by increasing the network parameters nor by decreasing the input signal resolution. Since the mass signature of cancer biomarkers are distributed over the whole mass spectrum, both locally- and globally-distributed patterns need to be captured to correctly classify the spectrum. By experiment, we show that employing dilated convolutions in the architecture of a CNN leads to a higher performance in tumor classification. Our proposed model outperforms the state-of-the-art for tumor classification in both clinical lung and bladder datasets by 1-3%.
LanguageEnglish
Title of host publicationMedical Imaging 2019: Digital Pathology
EditorsJohn E. Tomaszewski, Aaron D. Ward
PublisherSPIE
Number of pages8
DOIs
StatePublished - 18 Mar 2019
EventSPIE Medical Imaging: Digital Pathology - San Diego, United States
Duration: 16 Feb 201921 Feb 2019
Conference number: 10956
https://spie.org/MI/conferencedetails/digital-pathology

Publication series

NameProceedings of SPIE
Volume10956

Conference

ConferenceSPIE Medical Imaging
CountryUnited States
CitySan Diego
Period16/02/1921/02/19
Internet address

Fingerprint

Mass spectrometry
Tumors
Neural networks
Imaging techniques
Molecular imaging
Biomarkers
Pathology
Convolution
Learning algorithms
Learning systems
Computational complexity
Classifiers
Sampling
Molecules
Experiments

Cite this

van Kersbergen, J., Ghazvinian Zanjani, F., Zinger, S., van der Sommen, F., Balluff, B., Vos, D. R. N., ... de With, P. H. N. (2019). Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks. In J. E. Tomaszewski, & A. D. Ward (Eds.), Medical Imaging 2019: Digital Pathology [1095601] (Proceedings of SPIE; Vol. 10956). SPIE. DOI: 10.1117/12.2512360
van Kersbergen, Jannis ; Ghazvinian Zanjani, F. ; Zinger, Svitlana ; van der Sommen, Fons ; Balluff, Benjamin ; Vos, D.R.N. ; Ellis, S.R. ; Heeren, Ron M.A. ; Lucas, M. ; Marquering, H.A. ; Jansen, I. ; Savci-Heijink, Cemile Dilara ; de Bruin, D.M. ; de With, P.H.N./ Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks. Medical Imaging 2019: Digital Pathology. editor / John E. Tomaszewski ; Aaron D. Ward. SPIE, 2019. (Proceedings of SPIE).
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abstract = "Imaging mass spectrometry (IMS) is a novel molecular imaging technique to investigate how molecules are distributed between tumors and within tumor region in order to shed light into tumor biology or find potential biomarkers. Convolutional neural networks (CNNs) have proven to be very potent classifiers often outperforming other machine learning algorithms, especially in computational pathology. To overcome the challenge of complexity and high-dimensionality of the IMS data, the proposed CNNs are either very deep or use large kernels, which results in large amount of parameters and therefore a high computational complexity. An alternative is down-sampling the data, which inherently leads to a loss of information. In this paper, we propose using dilated CNNs as a possible solution to this challenge, since it allows for an increase of the receptive field size, neither by increasing the network parameters nor by decreasing the input signal resolution. Since the mass signature of cancer biomarkers are distributed over the whole mass spectrum, both locally- and globally-distributed patterns need to be captured to correctly classify the spectrum. By experiment, we show that employing dilated convolutions in the architecture of a CNN leads to a higher performance in tumor classification. Our proposed model outperforms the state-of-the-art for tumor classification in both clinical lung and bladder datasets by 1-3{\%}.",
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van Kersbergen, J, Ghazvinian Zanjani, F, Zinger, S, van der Sommen, F, 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 dilated convolutional neural networks. in JE Tomaszewski & AD Ward (eds), Medical Imaging 2019: Digital Pathology., 1095601, Proceedings of SPIE, vol. 10956, SPIE, SPIE Medical Imaging, San Diego, United States, 16/02/19. DOI: 10.1117/12.2512360

Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks. / van Kersbergen, Jannis; Ghazvinian Zanjani, F.; Zinger, Svitlana; van der Sommen, Fons; Balluff, Benjamin; Vos, D.R.N. ; Ellis, S.R. ; Heeren, Ron M.A.; Lucas, M. ; Marquering, H.A. ; Jansen, I.; Savci-Heijink, Cemile Dilara; de Bruin, D.M.; de With, P.H.N.

Medical Imaging 2019: Digital Pathology. ed. / John E. Tomaszewski; Aaron D. Ward. SPIE, 2019. 1095601 (Proceedings of SPIE; Vol. 10956).

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

TY - GEN

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

AU - van Kersbergen,Jannis

AU - Ghazvinian Zanjani,F.

AU - Zinger,Svitlana

AU - van der Sommen,Fons

AU - Balluff,Benjamin

AU - Vos,D.R.N.

AU - Ellis,S.R.

AU - Heeren,Ron M.A.

AU - Lucas,M.

AU - Marquering,H.A.

AU - Jansen,I.

AU - Savci-Heijink,Cemile Dilara

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N2 - Imaging mass spectrometry (IMS) is a novel molecular imaging technique to investigate how molecules are distributed between tumors and within tumor region in order to shed light into tumor biology or find potential biomarkers. Convolutional neural networks (CNNs) have proven to be very potent classifiers often outperforming other machine learning algorithms, especially in computational pathology. To overcome the challenge of complexity and high-dimensionality of the IMS data, the proposed CNNs are either very deep or use large kernels, which results in large amount of parameters and therefore a high computational complexity. An alternative is down-sampling the data, which inherently leads to a loss of information. In this paper, we propose using dilated CNNs as a possible solution to this challenge, since it allows for an increase of the receptive field size, neither by increasing the network parameters nor by decreasing the input signal resolution. Since the mass signature of cancer biomarkers are distributed over the whole mass spectrum, both locally- and globally-distributed patterns need to be captured to correctly classify the spectrum. By experiment, we show that employing dilated convolutions in the architecture of a CNN leads to a higher performance in tumor classification. Our proposed model outperforms the state-of-the-art for tumor classification in both clinical lung and bladder datasets by 1-3%.

AB - Imaging mass spectrometry (IMS) is a novel molecular imaging technique to investigate how molecules are distributed between tumors and within tumor region in order to shed light into tumor biology or find potential biomarkers. Convolutional neural networks (CNNs) have proven to be very potent classifiers often outperforming other machine learning algorithms, especially in computational pathology. To overcome the challenge of complexity and high-dimensionality of the IMS data, the proposed CNNs are either very deep or use large kernels, which results in large amount of parameters and therefore a high computational complexity. An alternative is down-sampling the data, which inherently leads to a loss of information. In this paper, we propose using dilated CNNs as a possible solution to this challenge, since it allows for an increase of the receptive field size, neither by increasing the network parameters nor by decreasing the input signal resolution. Since the mass signature of cancer biomarkers are distributed over the whole mass spectrum, both locally- and globally-distributed patterns need to be captured to correctly classify the spectrum. By experiment, we show that employing dilated convolutions in the architecture of a CNN leads to a higher performance in tumor classification. Our proposed model outperforms the state-of-the-art for tumor classification in both clinical lung and bladder datasets by 1-3%.

U2 - 10.1117/12.2512360

DO - 10.1117/12.2512360

M3 - Conference contribution

T3 - Proceedings of SPIE

BT - Medical Imaging 2019: Digital Pathology

PB - SPIE

ER -

van Kersbergen J, Ghazvinian Zanjani F, Zinger S, van der Sommen F, Balluff B, Vos DRN et al. Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks. In Tomaszewski JE, Ward AD, editors, Medical Imaging 2019: Digital Pathology. SPIE. 2019. 1095601. (Proceedings of SPIE). Available from, DOI: 10.1117/12.2512360