A User Guide to Image Quality Assessment for Artificial Intelligence | Webinars

A User Guide to Image Quality Assessment for Artificial Intelligence | Webinars

Tue, Nov 29, 2022 10:30A EST

ABOUT THIS WEBINAR

The size and number of images for image acquisition instruments and processing techniques have grown exponentially, along with the associated storage needs and network requirements. Most of these images are processed with machine learning techniques that rely on information that is invisible to the eye, but that can be revealed by observing fine correlations between pixels. Therefore, it is important that the raw images follow specific standards. Currently, however, there is a lack of standardized definitions and quality measures for these images. Dotphoton works to address these issues with associations such as QUAREP-Limi.

Dotphoton’s Arianne Bercowsky, Ph.D., presents insights into recent work involving standardization and image quality assessment to help future-proof image data. She discusses raw images from technical and physical perspectives, their main features, and how to evaluate image quality to ensure data-centric artificial intelligence (AI) and machine learning. She will also demonstrate Jetraw high-performance raw image compression technology, both as a software for biomedical and pharma, and as a field-programmable gate array (FPGA) implementation for camera manufacturers. The goal of Jetraw technology is to tackle the issues of big data, such as storage space and associated costs, CO2 emissions, and data transfer rates.

Bercowsky is joined by a representative from one of Dotphoton’s partners, Excelitas PCO, who presents a case study on the outcome of applying Jetraw compression to light-sheet microscopes using PCO cameras, demonstrated by Imperial College London.

 

Who should attend:

Researchers, engineers, and manufacturers who utilize artificial intelligence and machine learning. Lab managers, facility IT managers, clinicians, image analysts, and camera manufacturers who are looking to improve image quality assessment. Those who are interested in or work with light-sheet microscopy, drug development, and research in industries such as medicine, biomedicine, pharmaceuticals, and cancer research.

About the presenters:

Arianne Bercowsky, Ph.D., is an application specialist at Dotphoton. She received her doctorate in bioengineering and biotechnology at École Polytechnique Fédérale de Lausanne (EPFL) in the lab of professor Andrew C. Oates. Oates’ lab was one of Dotphoton’s first clients to integrate Jetraw technology into its image acquisition and processing workflow. The lab’s time-lapse data sets were measured in terabytes, which made data handling, storage, and transfer an issue. Oates’ lab has reduced data transfer time from two hours per dataset to just 15 minutes, reducing costs and the lab’s carbon footprint along the way. Bercowsky saw an immediate need for such technology in biomedical, medical imaging, and machine learning applications, so now she is passionate about helping researchers improve their image data acquisition and processing workflow.

About Dotphoton:

Dotphoton is a Swiss software company providing scalable solutions for large image data processing. Its flagship product, Jetraw, is the first raw compression technology perfectly suited for data-centric artificial intelligence (AI) and machine learning. Jetraw is delivered in both software and field-programmable gate array (FPGA) implementation and improves large data-set management for optical system manufacturers and for their end users. Dotphoton’s metrologically correct compression enables file size reduction, speeding up data transmission by at least 4×, and equally reducing storage costs and the carbon footprint. This allows companies to meet performance and environmental goals without sacrificing data quality. Dotphoton’s partners and clients include the European Space Agency, Bosch, leading life-science camera manufacturers, and biomedical labs.

Research & TechnologymetrologyMicroscopyartificial intelligenceimagingmachine visionVision Spectra

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