| About this Abstract |
| Meeting |
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Symposium
|
6th World Congress on Integrated Computational Materials Engineering (ICME 2022)
|
| Presentation Title |
ZEISS ZEN Intellesis - a Powerful and Open Machine Learning Ecosystem for Materials Microscopy |
| Author(s) |
Tobias Volkenandt, Robin White, William Harris |
| On-Site Speaker (Planned) |
William Harris |
| Abstract Scope |
In materials engineering at some point there is no way around analyzing the microstructure of real samples using microscopy. And machine learning (ML) has proven to be a valuable tool to derive tangible information from microscopic images for comparison with simulations and modelling. However, applying ML techniques in image segmentation or analysis is not straight-forward and typically comes with a steep learning curve. To overcome this hurdle, a powerful, open and easy-to-use ML ecosystem has been developed and integrated in the image acquisition and analysis suite ZEISS ZEN core. In this contribution we will discuss different features of the respective software module ZEN Intellesis and explain in detail how it can be used to implement ML. Starting from readily available pixel classification based on Random Forest segmentation, over import and execution of deep neural networks trained elsewhere, to an open cloud platform for effortless creation of deep learning models. |
| Proceedings Inclusion? |
Definite: Other |