What Is Deep Learning?

Check out this video to get an introduction to the benefits of self-learning microscopy!

• Label-free imaging
• Shorter exposure times
• Less photodamage
• Higher throughput
• More accurate results

Deep Learning and Microscopy

Artificial intelligence (AI) has experienced breakthroughs in vision applications, driven by deep neural networks (DNNs) and deep learning. While insufficient robustness and a lack of easy-to-use tools to reduce training time are often viewed as hurdles, Olympus’ scanR high-content screening system uses a self-learning microscopy approach that requires minimal human supervision.

In this webinar, we’ll discuss various deep-learning network models trained using the scanR system’s AI. We’ll also demonstrate how easy it is to train DNNs to perform segmentation tasks robustly in challenging scenarios without a lot of technical expertise. The performance of these DNNs exceeds traditional approaches and opens doors to new life science microscopy applications.

Agenda:

1. An introduction to deep learning and neural networks
2. Deep learning examples in object recognition and image processing
3. Applications of deep learning in microscopy and high-content screening
4. Conclusions and outlook

Daniel Bemmerl, Application Specialist at Olympus Soft Imaging Solutions

Daniel Bemmerl obtained his master’s degree in Molecular and Developmental Stem Cell Biology at Ruhr University Bochum. Before he joined Olympus Soft Imaging Solutions as an application specialist for high-content screening, he worked at the University of Münster, studying cell dynamics using advanced imaging techniques such as TIRF microscopy.

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