Recently added (10)#
Explainable AI for Computer Vision#
Robert Haase
Published 2025-03-09
Licensed CC-BY-4.0
In this slide deck we learn about the basics of Explainable Artificial Intelligence with a soft focus on Computer Vision. We take a deeper dive in one method: Gradient Class Activation Maps. Releated exercise materials are available online: https://haesleinhuepf.github.io/xai/
Learning and Training Bio-image Analysis in the Age of AI#
Robert Haase
Published 2025-04-07
Licensed CC-BY-4.0
The advent of large language models (LLMs) such as ChatGPT changes the way we analyse images. We ask LLMs to generate code, apply it to images and spend less time on learning implementation details. This also has impact on how we learn image analysis. While coding skills are still required, we can use LLMs to explain code, make proposals how to analyse the images and yet still decide how the analysis is done.
Nd2 does not open in Fiji Bio_formats 8.1.1#
Jaramillo Carlos
Published 2025-06-02
Licensed CC-BY-4.0
this file is a .nd2 image of a pollen grain taken with a Nikon 80i. It is in RGB and it is a stack of hundreds of Z layers
Nd2 does not open in Fiji Bio_formats 8.1.1 (additional files)#
Jonatan Bustos
Published 2025-05-23
Licensed CC-BY-4.0
This dataset contains 4 .nd2 image files of pollen grains captured using a Nikon 80i microscope. The files include both the original full-frame images and cropped Regions of Interest (ROIs) extracted from them. All images are in RGB format and include multiple Z-stack layers.
Towards open and standardised imaging data: an introduction to Bio-Formats, OME-TIFF, and OME-Zarr#
Josh Moore
Published 2025-05-28
Licensed CC-BY-4.0
https://www.ebi.ac.uk/training/events/towards-open-and-standardised-imaging-data-introduction-bio-formats-ome-tiff-and-ome-zarr/ Microscopy and bioimaging technologies are fundamental tools for exploring biological systems, generating large, multidimensional datasets rich in experimental detail. However, the bioimaging community has historically faced major challenges around data handling: vendor-specific proprietary formats, fragmented metadata storage, and increasingly large dataset sizes that outstrip traditional storage and computing solutions. In this webinar, key open technologies developed by the Open Microscopy Environment (OME) to address these challenges were presented. Specifically, the Bio-Formats library for accessing diverse proprietary file formats, the OME-TIFF standard for archival data storage, and the OME-Zarr format for cloud-native, scalable bioimaging workflows were presented.
[ELMI2025] Bridging communities with OME-Zarr#
Christian Schmidt, Aastha Mathur, Josh Moore
Published 2025-06-04
Licensed CC-BY-4.0
Presented at ELMI2025 https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/
[ELMI2025] The Road to OME-Zarr 1.0#
Josh Moore
Published 2025-06-05
Licensed CC-BY-4.0
Presented at https://www.embl.org/about/info/course-and-conference-office/events/elmi2025/ Abstract For over 20 years, the Open Microscopy Environment (OME) has developed tools and specifications to support bioimaging data sharing. Technologies such as Bio-Formats, OMERO, and OME-TIFF have helped researchers manage the growing size, complexity, and acquisition rates of imaging datasets. However, with increasing mandates for research data management, such as the Nelson memo in the United States, and the shift toward cloud-native workflows, the bioimaging community faces new challenges in ensuring scalable and FAIR data infrastructure. In 2024, following expanding community engagement, the focus of the Next-Generation File Format (NGFF) community was on building consensus around a Request for Comments (RFC) process. This collaborative effort has laid the foundation for future refinements and wider adoption. In parallel, we hosted the “OME2024 NGFF Challenge,” bringing together over the course of just four months hundreds of terabytes of data in a first prototype of federated image hosting, showcasing the power of OME-Zarr for handling large-scale, distributed datasets. In 2025, we are set to take a major step toward a stable FAIR solution with OME-Zarr 1.0. This milestone marks a crucial phase towards an international standard, providing an open, cloud-optimized, and scalable solution for handling terabyte- and petabyte-scale imaging data. The 1.0 release will introduce long-awaited transforms, enabling robust support for multimodal datasets, followed by collections and an extensibility mechanism to accommodate evolving scientific needs. These additions emphasize a solid foundation on which future capabilities can be built while providing the stability needed for broader adoption of the format. This presentation will outline the path to 1.0, including community-driven refinements, vendor engagement to ensure complete metadata representation, and alignment with global bioimaging initiatives. As imaging data continues to grow in scale and complexity, consensus-driven evolution of infrastructure will be key to ensuring a truly FAIR future for bioimaging.
[NFDI Tech Talk] Cloud Based Image Science#
Josh Moore, Yi Sun
Published 2025-06-02
Licensed CC-BY-4.0
Slides for the NFDI Tech Talk live streamed to https://www.youtube.com/live/bzfmE29S270 See http://nfdi.de/talks for more information.
https://zenodo.org/records/15575379
https://doi.org/10.5281/zenodo.15575379