• Grants

    Here are some grants in which we participate or have participated.

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    EXAMODE H2020 (EU)


    This project continuously produces exascale volumes of diverse data from distributed sources. The quantity of healthcare data especially stands out, with expected production over 2000 exabytes by 2020). It also stands out due to heterogeneity (many media, acquisition methods), included knowledge (e.g., diagnosis), and commercial value. The supervised nature of deep learning models requires large data that is labeled and annotated, which precludes models that extract knowledge and value. EXAMODE solves this by allowing easy, fast, and weekly supervised knowledge discovery of exascale heterogeneous data, limiting human interaction.


    The EXAMODE project has received funding from the European Union through the Horizon 2020 framework.


    More details at https://www.examode.eu

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    Clinical laboratories and R&D departments produce and analyze massive amounts of microscopic image data. During this project, MicroscopeIT provided prototypes to its partners for computations in a Software-as-a-Service (SaaS) model. Our partners included OptaTech BB (Berlin), the University of Zurich (Zurich), and Nikon (Zurich).


    The Fortissimo project has received funding from the European Union's Seventh Framework Programme for research, technological development, and demonstration under grant agreement No. 609029.


    More details at https://www.fortissimo-project.eu/experiments/608

  • Publications

    Here are some articles that demonstrate our expertise (not protected by NDAs).

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    Quantitative spatial analysis of haematopoiesis-regulating stromal cells in the bone marrow microenvironment by 3D microscopy

    Nature Communication, 2018.

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    Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform

    Journal of Royal Society Interface, 2017.

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    Randomized mutual exclusion on a multiple access channel

    Distributed Computing, 2016.

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    Distributed Alarming in the On-Duty and Off-Duty Models

    IEEE/ACM Trans. Netw., 2016.

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    Computable Bounds on the Spectral Gap for Unreliable Jackson Networks

    Advances in Applied Probability

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    A Robust Algorithm for Segmenting and Tracking Clustered Cells in Time-Lapse Fluorescent MicroscopyTitle Text

    IEEE Journal of Biomedical and Health Informatics, 2013.

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    An Empirical Comparison of Real-Time Dense Stereo Approaches for Use in the Automotive Environment

    EURASIP Journal on Image and Video Processing, 2012.