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Robust Prostate Cancer Classification with Siamese Neural Networks

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

Nuclear magnetic resonance (NMR) is a powerful and non–invasive diagnostic tool. However, NMR scanned images are often noisy due to patient motions or breathing. Although modern Computer Aided Diagnosis (CAD) systems, mainly based on Deep Learning (DL), together with expert radiologists, can obtain very accurate predictions, working with noisy data can induce a wrong diagnose or require a new acquisition, spending time and exposing the patient to an extra dose of radiation. In this paper, we propose a new DL model, based on a Siamese neural network, able to withstand random noise perturbations. We use data coming from the ProstateX challenge and demonstrate the superior robustness of our model to random noise compared to a similar architecture, albeit deprived of the Siamese branch. In addition, our approach is also resistant to adversarial attacks and shows overall better AUC performance.

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Notes

  1. 1.

    https://prostatex.grand-challenge.org/.

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Author information

Authors and Affiliations

  1. University of Florence, Via di S. Marta 3, 50139, Firenze, Italy

    Alberto Rossi

  2. University of Siena, Via Roma 56, 53100, Siena, Italy

    Alberto Rossi, Monica Bianchini & Franco Scarselli

Authors
  1. Alberto Rossi
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  2. Monica Bianchini
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  3. Franco Scarselli
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Corresponding author

Correspondence to Alberto Rossi .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Rossi, A., Bianchini, M., Scarselli, F. (2020). Robust Prostate Cancer Classification with Siamese Neural Networks. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64558-8

  • Online ISBN: 978-3-030-64559-5

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