This webinar was originally held on February 13th, 2024, and is now available for on demand viewing.
Duration: 1 hour
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Summary
Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software application, currently at version DeepTrack 2.2, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification.
Speaker
Giovanni Volpe
Professor, Dept. of Physics
University of Gothenburg
Sponsored by:
