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Computational Pathology and Ophthalmic Medical Image Analysis

First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings

  • Conference proceedings
  • © 2018

Overview

Editors:
  1. Danail Stoyanov

    1. University College London, London, UK

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  2. Zeike Taylor

    1. University of Leeds, Leeds, UK

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  3. Francesco Ciompi

    1. Radboud University Medical Center, Nijmegen, The Netherlands

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  4. Yanwu Xu

    1. Baidu, Beijing, China

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  5. Anne Martel

    1. Sunnybrook Health Science Centre, Toronto, Canada

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  6. Lena Maier-Hein

    1. Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

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  7. Nasir Rajpoot

    1. University of Warwick, Coventry, UK

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  8. Jeroen van der Laak

    1. Radboud University Medical Centre, Nijmegen, The Netherlands

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  9. Mitko Veta

    1. Eindhoven University of Technology, Eindhoven, The Netherlands

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  10. Stephen McKenna

    1. University of Dundee, Dundee, UK

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  11. David Snead

    1. University Hospital Coventry, Coventry, UK

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  12. Emanuele Trucco

    1. University of Dundee, Dundee, UK

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  13. Mona K. Garvin

    1. University of Iowa, Iowa City, USA

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  14. Xin Jan Chen

    1. Soochow University, Suzhou, China

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  15. Hrvoje Bogunovic

    1. Medical University of Vienna, Vienna, Austria

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11039)

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

Included in the following conference series:

Conference proceedings info: COMPAY 2018, OMIA 2018.

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Table of contents (40 papers)

  1. Front Matter

    Pages I-XVII
    Download chapter PDF

  2. First International Workshop on Computational Pathology, COMPAY 2018

    1. Front Matter

      Pages 1-1
      Download chapter PDF

    2. Improving Accuracy of Nuclei Segmentation by Reducing Histological Image Variability

      • Yusuf H. Roohani, Eric G. Kiss
      Pages 3-10

    3. Multi-resolution Networks for Semantic Segmentation in Whole Slide Images

      • Feng Gu, Nikolay Burlutskiy, Mats Andersson, Lena Kajland Wilén
      Pages 11-18

    4. Improving High Resolution Histology Image Classification with Deep Spatial Fusion Network

      • Yongxiang Huang, Albert Chi-Shing Chung
      Pages 19-26

    5. Construction of a Generative Model of H&E Stained Pathology Images of Pancreas Tumors Conditioned by a Voxel Value of MRI Image

      • Tomoshige Shimomura, Kugler Mauricio, Tatsuya Yokota, Chika Iwamoto, Kenoki Ohuchida, Makoto Hashizume et al.
      Pages 27-34

    6. Accurate 3D Reconstruction of a Whole Pancreatic Cancer Tumor from Pathology Images with Different Stains

      • Mauricio Kugler, Yushi Goto, Naoki Kawamura, Hirokazu Kobayashi, Tatsuya Yokota, Chika Iwamoto et al.
      Pages 35-43

    7. Role of Task Complexity and Training in Crowdsourced Image Annotation

      • Nadine S. Schaadt, Anne Grote, Germain Forestier, Cédric Wemmert, Friedrich Feuerhake
      Pages 44-51

    8. Capturing Global Spatial Context for Accurate Cell Classification in Skin Cancer Histology

      • Konstantinos Zormpas-Petridis, Henrik Failmezger, Ioannis Roxanis, Matthew Blackledge, Yann Jamin, Yinyin Yuan
      Pages 52-60

    9. Exploiting Multiple Color Representations to Improve Colon Cancer Detection in Whole Slide H&E Stains

      • Alex Skovsbo Jørgensen, Jonas Emborg, Rasmus Røge, Lasse Riis Østergaard
      Pages 61-68

    10. Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

      • Saad Ullah Akram, Talha Qaiser, Simon Graham, Juho Kannala, Janne Heikkilä, Nasir Rajpoot
      Pages 69-77

    11. Evaluating Out-of-the-Box Methods for the Classification of Hematopoietic Cells in Images of Stained Bone Marrow

      • Philipp Gräbel, Martina Crysandt, Reinhild Herwartz, Melanie Hoffmann, Barbara M. Klinkhammer, Peter Boor et al.
      Pages 78-85

    12. DeepCerv: Deep Neural Network for Segmentation Free Robust Cervical Cell Classification

      • O. U. Nirmal Jith, K. K. Harinarayanan, Srishti Gautam, Arnav Bhavsar, Anil K. Sao
      Pages 86-94

    13. Whole Slide Image Registration for the Study of Tumor Heterogeneity

      • Leslie Solorzano, Gabriela M. Almeida, Bárbara Mesquita, Diana Martins, Carla Oliveira, Carolina Wählby
      Pages 95-102

    14. Modality Conversion from Pathological Image to Ultrasonic Image Using Convolutional Neural Network

      • Takashi Ohnishi, Shu Kashio, Takuya Ogawa, Kazuyo Ito, Stanislav S. Makhanov, Tadashi Yamaguchi et al.
      Pages 103-111

    15. Structure Instance Segmentation in Renal Tissue: A Case Study on Tubular Immune Cell Detection

      • T. de Bel, M. Hermsen, G. Litjens, J. van der Laak
      Pages 112-119

    16. Cellular Community Detection for Tissue Phenotyping in Histology Images

      • Sajid Javed, Muhammad Moazam Fraz, David Epstein, David Snead, Nasir M. Rajpoot
      Pages 120-129

    17. Automatic Detection of Tumor Budding in Colorectal Carcinoma with Deep Learning

      • John-Melle Bokhorst, Lucia Rijstenberg, Danny Goudkade, Iris Nagtegaal, Jeroen van der Laak, Francesco Ciompi
      Pages 130-138

    18. Significance of Hyperparameter Optimization for Metastasis Detection in Breast Histology Images

      • Navid Alemi Koohbanani, Talha Qaisar, Muhammad Shaban, Jevgenij Gamper, Nasir Rajpoot
      Pages 139-147

    19. Image Magnification Regression Using DenseNet for Exploiting Histopathology Open Access Content

      • Sebastian Otálora, Manfredo Atzori, Vincent Andrearczyk, Henning Müller
      Pages 148-155
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  1. Computational Pathology and Ophthalmic Medical Image Analysis

Keywords

About this book

This book constitutes the refereed joint proceedings of the First International Workshop on Computational Pathology, COMPAY 2018, and the 5th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018.

The 19 full papers (out of 25 submissions) presented at COMPAY 2018 and the 21 full papers (out of 31 submissions) presented at OMIA 2018 were carefully reviewed and selected. The COMPAY papers focus on artificial intelligence and deep learning. The OMIA papers cover various topics in the field of ophthalmic image analysis.

Editors and Affiliations

  • University College London, London, UK

    Danail Stoyanov

  • University of Leeds, Leeds, UK

    Zeike Taylor

  • Radboud University Medical Center, Nijmegen, The Netherlands

    Francesco Ciompi

  • Baidu, Beijing, China

    Yanwu Xu

  • Sunnybrook Health Science Centre, Toronto, Canada

    Anne Martel

  • Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Lena Maier-Hein

  • University of Warwick, Coventry, UK

    Nasir Rajpoot

  • Radboud University Medical Centre, Nijmegen, The Netherlands

    Jeroen van der Laak

  • Eindhoven University of Technology, Eindhoven, The Netherlands

    Mitko Veta

  • University of Dundee, Dundee, UK

    Stephen McKenna, Emanuele Trucco

  • University Hospital Coventry, Coventry, UK

    David Snead

  • University of Iowa, Iowa City, USA

    Mona K. Garvin

  • Soochow University, Suzhou, China

    Xin Jan Chen

  • Medical University of Vienna, Vienna, Austria

    Hrvoje Bogunovic

Bibliographic Information

  • Book Title: Computational Pathology and Ophthalmic Medical Image Analysis

  • Book Subtitle: First International Workshop, COMPAY 2018, and 5th International Workshop, OMIA 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16 - 20, 2018, Proceedings

  • Editors: Danail Stoyanov, Zeike Taylor, Francesco Ciompi, Yanwu Xu, Anne Martel, Lena Maier-Hein, Nasir Rajpoot, Jeroen van der Laak, Mitko Veta, Stephen McKenna, David Snead, Emanuele Trucco, Mona K. Garvin, Xin Jan Chen, … Hrvoje Bogunovic

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/978-3-030-00949-6

  • Publisher: Springer Cham

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

  • Copyright Information: Springer Nature Switzerland AG 2018

  • Softcover ISBN: 978-3-030-00948-9Published: 14 September 2018

  • eBook ISBN: 978-3-030-00949-6Published: 13 September 2018

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: XVII, 347

  • Number of Illustrations: 135 b/w illustrations

  • Topics: Image Processing and Computer Vision, Artificial Intelligence, Arithmetic and Logic Structures, Probability and Statistics in Computer Science, Pattern Recognition

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