Improve Data Reproducibility Using Machine Learning and Cloud-based Image Analysis

The journey from image to insights can be challenging and hard to reproduce. According to a recent survey by Nature, more than 70% of researchers have tried and failed to reproduce another scientist's experiments. Many factors contributing to irreproducibility can be addressed via automation and collaborative work. Cloud offers the right infrastructure to automate image analysis tasks and to provide a collaborative environment. 

Cloud also enables location-independent access to tools whose importance is even more emphasized during the current COVID-19 pandemic. This webinar introduces the audience to the APEER platform that facilitates automation and collaborative work with microscopy images. 

The presentation provides an overview of APEER, including its new Machine Learning toolkit that makes deep learning accessible to all researchers. It also provides an example use case in which APEER helped automate a complex cell segmentation task.

Key Learnings

  • Why cloud-based image analysis?
  • ZEISS APEER: a free cloud platform
  • How APEER makes deep learning accessible for everyday use by researchers
  • Extending ZEN capabilities via APEER
  • Case study: How APEER addressed the automation of complex cell segmentation


Dr. Sreenivas Bhattiprolu

Head of Digital Solutions
 ZEISS Research Microscopy Solutions

Dr. Julia Mack

Professor in the Department of Medicine
Division of Cardiology at UCLA

Register for On-demand Webinar!