AI4Life

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Icy joins as a Community Partner!

Icy joins as a community partner!

by Carlos Garcia-López-de-Haro

The Bioimage Analysis software Icy has officially joined the BioImage Model Zoo as a Community Partner! This means that the Icy software will soon be compatible with the Deep Learning (DL) models present in the BioImage.io repository.

Icy is a powerful, open-source software designed for bioimage analysis, with features including visualization, annotation, graphical programming, and more. Now, with the compatibility with BioImage Model Zoo, Icy will further enhance its capabilities by leveraging the power of Deep Learning to analyze complex biological images better.

Meanwhile, Icy users will be encouraged to upload new models and datasets to the BioImage.io website, improving the collaboration and pushing the Bioimage Analysis field forward. The plugin to run Deep Learning models in Icy is in its final stage of development and it will be released soon. The Icy team is also providing the backend of their plugin as an independent Java library to run any Deep Learning model from various of the supported DL frameworks by the BioImage Model Zoo (Tensorflow 1, Tensorflow 2, Pytorch and Onnx) in an easy way.

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News

New videos in the AI4Life YouTube channel

First videos in the AI4Life YouTube channel

 

The AI4Life YouTube channel is officially inaugurated! It features two training videos currently, with more to come in the future. The first two videos mark the beginning of a new playlist for training, showing how to upload models to the BioImage Model Zoo and the cross-compatibility of these models, which allows researchers to use them with different software tools and platforms.

We look forward to seeing more content on this channel in the future.

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event report

Outcomes of the hackathon “Deep Learning in Java”

Outcomes of the Hackathon “deep Learning in Java”

Milan, 6-10 February 2023

by Florian Jug

Global BioImaging and AI4Life organized a hackathon in Milan from February 6-10, 2023. The overarching goal of this event was to improve the accessibility of Deep Learning methods in Java-based image analysis tools and libraries. The event was held at the Human Technopole and was attended by a total of 21 participants from various parts of the world. 

The participants, representing tools such as bioimage.io, deepImageJ, Fiji, Icy, ImageJ, ImJoy, and QuPath, self-organized into topic-groups on day one and then tackled various challenges to bridge the system gap between typically python-based deep learning methods and Java (i.e. ImgLib2 based) image processing. 

These topic-groups made significant progress on different fronts over the 5-day event. A more in-depth report will soon be made available as an BioHackrXiv preprint. Among the highlights was the integration of a library by Carlos Garcia and colleagues (model-runner-java) into deepImageJ (and therefore into Fiji) and several other participants using this new way of running deep learning models on images opened in ImgLib2 containers (e.g. directly from Fiji). This was even pushed to extremes by combining the execution of models live from within BigDataViewer, e.g., enabling lazy prediction on terabyte sized datasets.

Additionally, another topic-group explored alternative ways to use the model-runner-java library, by directly sharing memory between native python processes and running Java VMs. Similar solutions exist (see for example imglyb or PyImageJ), but the newly explored idea is not any longer based on sub-processes but instead on inter-process communication. The big advantage of this approach is that parallel processes can be started independently, hook into each other on demand using shared memory, work together but die alone.

All participants are now continuing to flesh out the work that was started during the event and releases of updated versions of deepImageJ and a Fiji and Icy based deep learning integration are on their way. These updates will benefit hundreds of users world-wide.

 
AcknowledgementS
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upcoming events

Course Microscopy data analysis: machine learning and the BioImage Archive

Microscopy data analysis: machine learning and the BioImage Archive

Date

Venue

Description

 

22-26 May 2023

Online

This virtual course will show how public bioimaging data resources, centred around the BioImage Archive, enable and enhance machine learning based image analysis. The content will explore a variety of data types including electron and light microscopy and miscellaneous or multi-modal imaging data at the cell and tissue scale. Participants will cover contemporary biological image analysis with an emphasis on machine learning methods, as well as how to access and use images from databases. Further instruction will be offered using applications such as ZeroCostDL4Mic, ilastik, ImJoy, the BioImage Model Zoo, and CellProfiler.

Contact information

Program, registration and more can be found here.