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Digital Pathology

15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings

  • Conference proceedings
  • © 2019

Overview

Editors:
  1. Constantino Carlos Reyes-Aldasoro

    1. Electrical Engineering, City, University of London, London, UK

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  2. Andrew Janowczyk

    1. Case Western Reserve University, Cleveland, USA

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  3. Mitko Veta

    1. Eindhoven University of Technology, Eindhoven, The Netherlands

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  4. Peter Bankhead

    1. University of Edinburgh, Edinburgh, UK

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  5. Korsuk Sirinukunwattana

    1. University of Oxford, Oxford, UK

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11435)

Part of the book sub series: Image Processing, Computer Vision, Pattern Recognition, and Graphics (LNIP)

Included in the following conference series:

Conference proceedings info: ECDP 2019.

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Table of contents (21 papers)

  1. Front Matter

    Pages i-xi
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  2. Image Datasets and Virtual Staining

    1. Front Matter

      Pages 1-1
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    2. Bringing Open Data to Whole Slide Imaging

      • Sébastien Besson, Roger Leigh, Melissa Linkert, Chris Allan, Jean-Marie Burel, Mark Carroll et al.
      Pages 3-10

    3. PanNuke: An Open Pan-Cancer Histology Dataset for Nuclei Instance Segmentation and Classification

      • Jevgenij Gamper, Navid Alemi Koohbanani, Ksenija Benet, Ali Khuram, Nasir Rajpoot
      Pages 11-19

    4. Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs

      • Jacob Carse, Stephen McKenna
      Pages 20-27

    5. Patch Clustering for Representation of Histopathology Images

      • Wafa Chenni, Habib Herbi, Morteza Babaie, Hamid R. Tizhoosh
      Pages 28-37

    6. Virtually Redying Histological Images with Generative Adversarial Networks to Facilitate Unsupervised Segmentation: A Proof-of-Concept Study

      • Michael Gadermayr, Barbara M. Klinkhammer, Peter Boor
      Pages 38-46

    7. Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

      • Amal Lahiani, Jacob Gildenblat, Irina Klaman, Shadi Albarqouni, Nassir Navab, Eldad Klaiman
      Pages 47-55

    8. Evaluation of Colour Pre-processing on Patch-Based Classification of H&E-Stained Images

      • Francesco Bianconi, Jakob N. Kather, Constantino C. Reyes-Aldasoro
      Pages 56-64

  3. Segmentation

    1. Front Matter

      Pages 65-65
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    2. Automated Segmentation of DCIS in Whole Slide Images

      • Nikhil Seth, Shazia Akbar, Sharon Nofech-Mozes, Sherine Salama, Anne L. Martel
      Pages 67-74

    3. A Two-Stage U-Net Algorithm for Segmentation of Nuclei in H&E-Stained Tissues

      • Amirreza Mahbod, Gerald Schaefer, Isabella Ellinger, Rupert Ecker, Örjan Smedby, Chunliang Wang
      Pages 75-82

    4. Automatic Detection of Tumor Buds in Pan-Cytokeratin Stained Colorectal Cancer Sections by a Hybrid Image Analysis Approach

      • Matthias Bergler, Michaela Benz, David Rauber, David Hartmann, Malte Kötter, Markus Eckstein et al.
      Pages 83-90

    5. Improving Prostate Cancer Detection with Breast Histopathology Images

      • Umair Akhtar Hasan Khan, Carolin Stürenberg, Oguzhan Gencoglu, Kevin Sandeman, Timo Heikkinen, Antti Rannikko et al.
      Pages 91-99

    6. Multi-tissue Partitioning for Whole Slide Images of Colorectal Cancer Histopathology Images with Deeptissue Net

      • Jun Xu, Chengfei Cai, Yangshu Zhou, Bo Yao, Geyang Xu, Xiangxue Wang et al.
      Pages 100-108

    7. Rota-Net: Rotation Equivariant Network for Simultaneous Gland and Lumen Segmentation in Colon Histology Images

      • Simon Graham, David Epstein, Nasir Rajpoot
      Pages 109-116

    8. Histopathological Image Analysis on Mouse Testes for Automated Staging of Mouse Seminiferous Tubule

      • Jun Xu, Haoda Lu, Haixin Li, Xiangxue Wang, Anant Madabhushi, Yujun Xu
      Pages 117-124

    9. Deep Features for Tissue-Fold Detection in Histopathology Images

      • Morteza Babaie, Hamid R. Tizhoosh
      Pages 125-132

  4. Computer-Assisted Diagnosis and Prognosis

    1. Front Matter

      Pages 133-133
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    2. A Fast Pyramidal Bayesian Model for Mitosis Detection in Whole-Slide Images

      • Santiago López-Tapia, José Aneiros-Fernández, Nicolás Pérez de la Blanca
      Pages 135-143
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  1. Digital Pathology

Keywords

About this book

This book constitutes the refereed proceedings of the 15th European Congress on Digital Pathology,  ECDP 2019, held in Warwick, UK in April 2019. The 21 full papers presented in this volume were carefully reviewed and selected from 30 submissions. The congress theme will be Accelerating Clinical Deployment, with a focus on computational pathology and leveraging the power of big data and artificial intelligence to bridge the gaps between research, development, and clinical uptake.

Editors and Affiliations

  • Electrical Engineering, City, University of London, London, UK

    Constantino Carlos Reyes-Aldasoro

  • Case Western Reserve University, Cleveland, USA

    Andrew Janowczyk

  • Eindhoven University of Technology, Eindhoven, The Netherlands

    Mitko Veta

  • University of Edinburgh, Edinburgh, UK

    Peter Bankhead

  • University of Oxford, Oxford, UK

    Korsuk Sirinukunwattana

Bibliographic Information

  • Book Title: Digital Pathology

  • Book Subtitle: 15th European Congress, ECDP 2019, Warwick, UK, April 10–13, 2019, Proceedings

  • Editors: Constantino Carlos Reyes-Aldasoro, Andrew Janowczyk, Mitko Veta, Peter Bankhead, Korsuk Sirinukunwattana

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-23937-4

  • Publisher: Springer Cham

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer Nature Switzerland AG 2019

  • Softcover ISBN: 978-3-030-23936-7Published: 03 July 2019

  • eBook ISBN: 978-3-030-23937-4Published: 03 July 2019

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XI, 192

  • Number of Illustrations: 8 b/w illustrations, 85 illustrations in colour

  • Topics: Image Processing and Computer Vision, Artificial Intelligence

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