AI-based Slide Quality Control Workflows for Life Science and Clinical Applications

Date: 18 April 2024
Time: 8:00 – 9:00 PDT | 11:00 – 12:00 EDT | 16:00 – 17:00 BST
Location: Webinar

Learn how SlideQC BF supports automated quality control workflows in the life sciences and in clinical applications for H&E and IHC images

Summary

Implementation of image quality control procedures is crucial to identify artifacts that might negatively impact the diagnostic procedure or performance of image analysis algorithms.  

In this 1-hour webinar learn about SlideQC BF 3.0, an AI-based image quality control network that detects and segments most common artifacts including air bubbles, debris, folds, out of focus regions, and pen marker in both hematoxylin and eosin (H&E) and immunohistochemistry (IHC) images. 

Learn how you can use it for both research and clinical workflows and discover how SlideQC can be used to automatically triage slides and improve workflow efficiency. 

Learning Objectives

  • Learn how the SlideQC BF 3.0 network was developed and validated  
  • Learn how to use SlideQC BF in HALO AI  
  • Learn how to deploy SlideQC BF in a clinical environment with HALO AP® 
  • Learn how to automatically triage slides using SlideQC BF 

Presenters

Daniela Rodrigues 
Imaging Scientist, AI Diagnostics
Indica Labs

Daniela Rodrigues earned her degree in Pathologic, Cytologic, and Thanatological Anatomy from the Polytechnic Institute of Porto. She received her master’s in Quality Control from the Faculty of Pharmacy in 2018 and undertook a specialization in Medical Informatics from the Faculty of Medicine of the University of Porto in 2021. She previously worked as a research fellow in Neuroscience and Toxicology and grew her skill set in microscopy and digital pathology-based image analysis while working at the microscopy core facility at i3S – Instituto de Investigação e Inovação em Saúde, University of Porto, where she provided image analysis support to several digital pathology projects. Currently, she works as an Imaging Scientist in the AI Diagnostics group managing the development of multiple AI-based products. 

Katie McKinley 
VP, Clinical Applications 
Indica Labs

Katie is responsible for the commercial growth of HALO AP®, Indica Labs’ first analysis software designed specifically for clinical workflows. Working closely with clinical laboratories, she applies her experience of the clinical market, and pathology workflow optimization to support institutions as they transition away from glass. Katie has held a variety of customer-facing roles over the years, entering the digital pathology industry in 2009. 

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