Deep Learning Approaches in Microscopy
The field of artificial intelligence (AI) has recently seen an outstanding run of breakthroughs in vision applications, driven by deep neural networks (DNNs) and deep learning. However, insufficient robustness and a lack of easy-to-use tools to save time on training are often perceived as hurdles to practical application.
In this webinar we’ll show examples of various deep learning network models, trained using Olympus scanR AI high content screening system. scanR uses a self-learning microscopy approach that requires minimal human supervision.
We will 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 of Recorded Webinar:
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
Our Webinar Speaker
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 application specialist for high content screening he worked at the University of Münster, studying cell dynamics using advanced imaging techniques such as TIRF microscopy.