AI for Robust and Easy to Use Automated Image Analysis
Discover the possibilities in AI webinar (2 Apr, 10am CEST)
Artificial intelligence (AI) has recently had an outstanding run of breakthroughs in vision applications, driven by deep neural networks (DNNs) and deep learning. However, insufficient robustness and a lack of simple tools to save time on training are often perceived as hurdles to practical application.
In an upcoming Olympus AI webinar, we will show how DNNs carry out robust image quantification in challenging scenarios without requiring a lot of technical expertise.
Find out more at www.olympus.eu/deep-learning-webinar
In this webinar, we will show examples of DNNs that are trained using Olympus’ scanR AI-based high content screening system. scanR uses a self-learning microscopy approach that is highly reliable and requires minimal human supervision.
We will demonstrate how easy it is to train DNNs to perform image analysis tasks robustly – even under challenging conditions. The performance of these DNNs often exceeds traditional approaches and opens doors to new life science microscopy applications.
One example that clearly highlights the benefits of AI is the automated analysis of cell populations in microwell plates without fluorescent labeling. Methods based on AI are ideally suited to address this challenge and we will demonstrate how AI can reliably detect and analyze cell nuclei from brightfield images with an accuracy that exceeds fluorescence-based methods.
Learn more about this and other applications of AI in image analysis during our webinar (2 April, 10–11am CEST): www.olympus.eu/deep-learning-webinar