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

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Advances in Computational Intelligence (IWANN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10306))

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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.

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Notes

  1. 1.

    IMEDIATREAT Project, https://sites.google.com/site/imediatreat/.

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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.

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania

    Stefan Postavaru

  2. Bitdefender, Bucharest, Romania

    Stefan Postavaru

  3. Faculty of Sciences, University of Craiova, Craiova, Romania

    Ruxandra Stoean & Catalin Stoean

  4. School of Telecommunication Engineering, University of Malaga, Málaga, Spain

    Gonzalo Joya Caparros

Authors
  1. Stefan Postavaru
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  2. Ruxandra Stoean
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  3. Catalin Stoean
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  4. Gonzalo Joya Caparros
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Corresponding author

Correspondence to Ruxandra Stoean .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Vilanova i la Geltrú, Barcelona, Spain

    Andreu Catala

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Postavaru, S., Stoean, R., Stoean, C., Caparros, G.J. (2017). Adaptation of Deep Convolutional Neural Networks for Cancer Grading from Histopathological Images. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-59147-6_4

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