Delivering high-quality, quantitative data from digital pathology images is one of OracleBio’s core values. Tools such as Visiopharm enable us to achieve this by building robust, AI-powered image analysis workflows.

In this masterclass, we explore the QC strategies used to ensure accuracy and reproducibility in the following case studies:

• The creation of a pathologist-validated and scalable image analysis workflow for a retrospective clinical study involving over 2,200 IHC-stained colorectal polyp whole slide images.
• The use of Deep Learning apps to segment morphologically distinct cell subpopulations and the Phenoplex workflow to streamline the thresholding process for spatial phenotyping in multiplex IF-stained pre-clinical tissue.

Karen McClymont, Image Analysis Project Manager, OracleBio

With a PhD in Biochemistry and her active involvement and support in the analysis of some of OracleBio’s most complex studies, Karen plays a key role leading OracleBio’s image analysis projects and overseeing the team’s continued development.

Gabriel Reines March, R&D Project Manager, OracleBio

With a PhD in Biomedical Image Processing and a background in Electrical Engineering, Gabriel is the OracleBio R&D team lead. He oversees and manages the group’s project pipeline and ensures that the company stays at the bleeding edge of the industry.

Gabriel also manages OracleBio’s involvement in the INCISE project – a collaborative effort between industry, academia and the UK’s National Health Service.