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Exploring Image Analysis Innovation with Trevor McKee of Pathomics.io

If you work in digital pathology, you likely rely on image analysis tools to gain insights from complex visual data. But how do you stay on top of the latest innovations in this fast-evolving field?

In this podcast episode together with Trevor McKee, CEO of Pathomics.io, we discuss innovation in image analysis using open source tools.

Pathomics takes an innovative approach by building image analysis solutions on open source platforms like QuPath. As Trevor explained, open source fosters collaboration, democratizes access, and drives rapid advances - key in a fast-moving field like digital pathology. This enables rapid progress that proprietary systems can't match.

Trevor's Career Journey

Trevor’s journey lead him from chemical engineering into pioneering image analysis, inspired by solving complex biological problems. His diverse experiences, from photon imaging at MIT to leading a core lab facility, fueled a passion for leveraging image analysis to extract insights. Today, in addition to leading Pathomics.io he is an Adjunct Lecturer at the University of Toronto,  and the Chief Scientific Officer at BioCache™ Lab Solutions.

Transparent and Reproducible Image Analysis & Explainable AI

A core ethos at Pathomics is making image analysis transparent and reproducible.  through explainable AI techniques. Tools like XGBoost create models that are easier to interpret than "black-box" end-to-end neural networks. This builds trust and acceptance among the scientific community.

Streamlining Workflows

In addition, Pathomics develops solutions to streamline clients' image analysis workflows. For example, their Universal StarDist plugin makes it easy to run advanced models like StarDist in QuPath. Overall, the goal is to automate tedious tasks so you can concentrate on high-value decision making.

The Future of Image Analysis

Looking ahead, Trevor shared his vision for an AI-powered online platform enabling users to go seamlessly from images to insights. He also discussed open wikis to prevent redundant work and encourage knowledge sharing as the field rapidly evolves.

Trevor  plans to launch it to catalogue digital pathology resources such as image analysis focused machine learning papers  to prevent redundant research work and encourage knowledge sharing as the field rapidly evolves.. It aligns with his commitment to open science and community knowledge sharing.

Key Takeaways

I came away from our wide-ranging discussion with an insider’s view of the huge potential of image analysis to transform digital pathology. By leveraging open source tools and staying atop the latest advances, you can work smarter and unlock new capabilities.

So tune in to explore these innovations and more from a leader in the field! The episode provides practical insights you can apply to make the most of the newest techniques

THIS EPISODE'S RESOURCES:

• Pathomics.io website 
• Pathomics Wiki with tissue image analysis papers
• Interested in contributing to Pathomics Wiki? Submit your entry here.
• List of open source softwa

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