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Adaptation of Deep Convolutional Neural Networks for Cancer Grading from Histopathological Images

  • Stefan Postavaru
  • Ruxandra Stoean
  • Catalin Stoean
  • Gonzalo Joya Caparros
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10306)

Abstract

The paper addresses the medical challenge of interpreting histopathological slides through expert-independent automated learning with implicit feature determination and direct grading establishment. Deep convolutional neural networks model the image collection and are able to give a timely and accurate support for pathologists, who are more than often burdened by large amounts of data to be processed. The paradigm is however known to be problem-dependent in variable setting, therefore automatic parametrization is also considered. Due to the large necessary runtime, this is restricted to kernel size optimization in each convolutional layer. As processing time still remains considerable for five variables, a surrogate model is further constructed. Results support the use of the deep learning methodology for computational assistance in cancer grading from histopathological images.

Keywords

Image processing Histopathological slides Classification Deep convolutional neural networks Parametrization 

Notes

Acknowledgments

The second and third authors acknowledge the support of the research grant no. 26/2014, IMEDIATREAT - Intelligent Medical Information System for the Diagnosis and Monitoring of the Treatment of Patients with Colorectal Neoplasm -of the Romanian Ministry of National Education - Research and the Executive Agency for Higher Education Research Development and Innovation Funding.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Stefan Postavaru
    • 1
    • 2
  • Ruxandra Stoean
    • 3
  • Catalin Stoean
    • 3
  • Gonzalo Joya Caparros
    • 4
  1. 1.Faculty of Mathematics and Computer ScienceUniversity of BucharestBucharestRomania
  2. 2.BitdefenderBucharestRomania
  3. 3.Faculty of SciencesUniversity of CraiovaCraiovaRomania
  4. 4.School of Telecommunication EngineeringUniversity of MalagaMálagaSpain

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