ExaMode

Background

Big volumes of diverse data from distributed sources are continuously produced. Health-care data is expected to be over 2000 exabytes in 2020. Supervised deep learning models, require large amount of annotated data. Considering the available data, such annotations are impractical. The ExaMode project is a collaboration between universities and industries and addresses this impracticality by applying weakly supervised deep learning based on diverse data from distributed sources.

Aim

The project aims to solve: "weakly supervised knowledge discovery of exascale heterogeneous data.’’

Radboudumc is responsible for the following objectives:

  • Develop deep learning methods for detection and semantic segmentation of regions of interest in histopathology whole-slide images.
  • Develop deep learning methods for whole-slide image classification using image-level target labels extracted from pathology reports.
  • Develop methods of whole-slide image compression to allow end-to-end training of deep neural networks with whole-slide image data.
  • Develop efficient parallel implementations of researched ANNs dealing with both shared and distributed memory environments.
  • Make developed methods available via a web-based platform.
  • Facilitate integration of developed methods in digital pathology workflow and development of decision support algorithms.

Funding

People

Francesco Ciompi

Francesco Ciompi

Assistant Professor

Computational Pathology Group

Jeroen van der Laak

Jeroen van der Laak

Associate Professor

Computational Pathology Group

Mart van Rijthoven

Mart van Rijthoven

PhD Candidate

Computational Pathology Group

Miriam Groeneveld

Miriam Groeneveld

Research Software Engineer

Computational Pathology Group

Witali Aswolinskiy

Witali Aswolinskiy

Postdoctoral Researcher

Computational Pathology Group

Elke Loskamp-Huntink

Elke Loskamp-Huntink

Study Manager

Computational Pathology Group

Maud Wekking

Maud Wekking

Research Technician

Computational Pathology Group

Publications

  • D. Tellez, G. Litjens, J. van der Laak and F. Ciompi, "Neural Image Compression for Gigapixel Histopathology Image Analysis", IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019;58:101544. Abstract/PDF DOI PMID Cited by ~7
  • Z. Swiderska-Chadaj, H. Pinckaers, M. van Rijthoven, M. Balkenhol, M. Melnikova, O. Geessink, Q. Manson, M. Sherman, A. Polonia, J. Parry, M. Abubakar, G. Litjens, J. van der Laak and F. Ciompi, "Learning to detect lymphocytes in immunohistochemistry with deep learning", Medical Image Analysis, 2019;58:101547. Abstract/PDF DOI PMID Cited by ~9
  • J. Bokhorst, H. Pinckaers, P. van Zwam, I. Nagetgaal, J. van der Laak and F. Ciompi, "Learning from sparsely annotated data for semantic segmentation in histopathology images", Medical Imaging with Deep Learning, 2019;102:81-94. Abstract/PDF Url Cited by ~2