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Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis

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Automated Reasoning for Systems Biology and Medicine

Part of the book series: Computational Biology ((COBO,volume 30))

Abstract

In the recent years, deep learning based methods and, in particular, convolutional neural networks, have been dominating the arena of medical image analysis. This has been made possible both with the advent of new parallel hardware and the development of efficient algorithms. It is expected that future advances in both of these directions will increase this domination. The application of deep learning methods to medical image analysis has been shown to significantly improve the accuracy and efficiency of the diagnoses. In this chapter, we focus on applications of deep learning in microscopy image analysis and digital pathology, in particular. We provide an overview of the state-of-the-art methods in this area and exemplify some of the main techniques. Finally, we discuss some open challenges and avenues for future work.

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Notes

  1. 1.

    Actually b can be considered as a special weight \(w_0\) associated with a special input \(x_0\) which has a constant value 1. In this way the transfer function becomes slightly simpler \(\sigma (\mathbf {w}^T \mathbf {x})\). However, for the sake of clarity here we keep these two parameters separately.

  2. 2.

    Actually this is a definition of a cross-correlation which is slightly different than the usual mathematical notion of convolution, but in the machine learning practice this is how the convolution operation is implemented [23].

  3. 3.

    In principle, one can unfold the \(m \times n\) covered rectangular patch of the input and the filter into l-dimensional vectors, where \(l = m \times n\). In this way, “*” becomes real a dot product between the vectors. Also, a bias element can be added, like in the traditional neural networks.

  4. 4.

    In recent years, there is growing a trend to use fully convolutional networks in which the fully connected layers are implemented by means of convolutional layers.

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Acknowledgements

The authors would like to thank the anonymous reviewers as well as Stojan Trajanovski for their comments and suggestions that contributed to the final version of this paper.

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Authors and Affiliations

  1. Biomedical Engineering Department, Eindhoven University of Technology, Eindhoven, The Netherlands

    Dragan Bošnački, Natal van Riel & Mitko Veta

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  1. Dragan Bošnački
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Correspondence to Dragan Bošnački .

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  1. Department of Computer Science and Technology, University of Cambridge, Cambridge, UK

    Pietro Liò

  2. School of Computing, Newcastle University, Newcastle, UK

    Paolo Zuliani

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Bošnački, D., van Riel, N., Veta, M. (2019). Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis. In: Liò, P., Zuliani, P. (eds) Automated Reasoning for Systems Biology and Medicine. Computational Biology, vol 30. Springer, Cham. https://doi.org/10.1007/978-3-030-17297-8_17

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