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

Jannis van Kersbergen*, F.G. Zanjani, S. Zinger, Fons van der Sommen, Benjamin Balluff, Naomi Vos, Shane Ellis, Ron Heeren, Marit Lucas, Henk Marquering, I. Jansen, C. D. Savci-Heijink, D.M. de Bruin, Peter H. N. de With

*Corresponding author for this work

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


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%.
Original languageEnglish
Title of host publicationMedical Imaging 2019: Digital Pathology
EditorsJE Tomaszewski, AD Ward
PublisherSPIE-Society of Photo-Optical Instrumentation Engineers
Number of pages8
Publication statusPublished - 2019

Publication series

SeriesProceedings of SPIE - The International Society for Optical Engineering


  • computational pathology
  • convolutional neural networks
  • mass spectrometry imaging
  • cancer detection
  • dilated convolution

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