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Use of synthetic data in AI development
Insights from a case study in Cryo-EM image analysis
IT
Introduction
Target operating process and challenges
Machine learning methodology selection
Model validation and the role of synthetic data
Case study setup and results
Summary and outlook
Target operating process and challenges
Machine learning methodology selection
Model validation and the role of synthetic data
Case study setup and results
Summary and outlook
Keywords, Unsupervised Machine Learning, Synthetic Data, Validation, Adeno-Associated Viruses (AAV), Cryo-EM
Dr. Georg Bunzel1, Dr. Karl Bertram1, Dr. Nico Erdmann2, Martin Heitmann3, Carsten Jasper4 · 1ATEM SD GmbH, Remscheid 2 · Deloitte Wirtschaftsprüfungsgesellschaft GmbH, Düsseldorf 3 · d-fine GmbH, Frankfurt 4 · Charles River Microbial Solutions Germany GmbH, Kaarst
Abstract
Artificial Intelligence (AI), and in particular Machine Learning (ML) methods, unlock new approaches to digitalization through advanced data analysis – for instance for image processing. At the same time, novel high-resolution methods for collecting image data are opening up new use cases in analytical and quality control applications, like the use of cryogenic electron microscopy (...)
