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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

Uittreksel

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%.
TaalEngels
TitelMedical Imaging 2019: Digital Pathology
RedacteurenJohn E. Tomaszewski, Aaron D. Ward
UitgeverijSPIE
Aantal pagina's8
DOI's
StatusGepubliceerd - 18 mrt 2019
EvenementSPIE Medical Imaging: Digital Pathology - San Diego, Verenigde Staten van Amerika
Duur: 16 feb 201921 feb 2019
Congresnummer: 10956
https://spie.org/MI/conferencedetails/digital-pathology

Publicatie series

NaamProceedings of SPIE
Volume10956

Congres

CongresSPIE Medical Imaging
LandVerenigde Staten van Amerika
StadSan Diego
Periode16/02/1921/02/19
Internet adres

Vingerafdruk

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

Citeer dit

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 (editors), 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. redacteur / John E. Tomaszewski ; Aaron D. Ward. SPIE, 2019. (Proceedings of SPIE).
@inproceedings{48181d86453f44cfb9a972d8e75fb6d0,
title = "Cancer detection in mass spectrometry imaging data by dilated convolutional neural networks",
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{\%}.",
author = "{van Kersbergen}, Jannis and {Ghazvinian Zanjani}, F. and Svitlana Zinger and {van der Sommen}, Fons and Benjamin Balluff and D.R.N. Vos and S.R. Ellis and Heeren, {Ron M.A.} and M. Lucas and H.A. Marquering and I. Jansen and Savci-Heijink, {Cemile Dilara} and {de Bruin}, D.M. and {de With}, P.H.N.",
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language = "English",
series = "Proceedings of SPIE",
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editor = "Tomaszewski, {John E.} and Ward, {Aaron D.}",
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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 (redactie), Medical Imaging 2019: Digital Pathology., 1095601, Proceedings of SPIE, vol. 10956, SPIE, San Diego, Verenigde Staten van Amerika, 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. redactie / John E. Tomaszewski; Aaron D. Ward. SPIE, 2019. 1095601 (Proceedings of SPIE; Vol. 10956).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer 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.

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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, redacteurs, Medical Imaging 2019: Digital Pathology. SPIE. 2019. 1095601. (Proceedings of SPIE). Beschikbaar vanaf, DOI: 10.1117/12.2512360