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Impact AI
Trailer
Bonus
Episode 70
Season 1
Unlocking Blood Cell Morphology with Erez Naaman from Scopio Labs
In this episode, I sit down with Erez Naaman, co-founder and CTO of Scopio Labs, to delve into the transformative potential of AI in healthcare, particularly in blood cell morphology analysis. Erez shares the intriguing journey behind the inception of Scopio Labs which was driven by a desire to revolutionize healthcare practices. Discover how Scopio Labs' platforms digitize and streamline the process of blood cell analysis and the pivotal role of machine learning in distinguishing and classifying various cell types. Gain insights into the significance of data collection and algorithm development, the evolution of AI infrastructure over the past decade, regulatory considerations on product development, and more. He also shares invaluable insights for AI startup leaders, the future trajectory of Scopio Labs, and the profound impact envisioned for the healthcare landscape. Join me as we explore the intersection of AI and healthcare innovation with Erez Naaman.
Key Points:
- Eres shares his professional background and his path to founding Scopio Labs.
- Revolutionizing healthcare through AI-driven blood cell morphology analysis.
- The pivotal role machine learning plays in distinguishing and classifying various cell types.
- Discover the challenges of working with blood smear images; particularly for training models.
- Learn about the differences between regulated and nonregulated machine learning.
- AI infrastructure development and the associated regulatory considerations.
- Explore his approach to developing new machine learning products or features.
- Hear why he chooses to prioritize the end-user experience during development.
- Advice for budding entrepreneurs and the future trajectory of Scopio Labs.
Quotes:
“In terms of the approach [to AI], I think we saw it the same way that we do today in terms of its importance but I think that the infrastructure for using ML has greatly evolved.” — Erez Naaman
“Getting a large enough data set to get a reliable classification on specific more rare cell types is the most difficult problem in my opinion.” — Erez Naaman
“In a way, we look at it backward. Machine learning is a tool and not a goal. So, we always start with the patient in mind or the user.” — Erez Naaman
“Everyone is dealing with AI and so the front runners are clearly becoming the leaders with time. So, it is much easier to choose the right tools for every task as time progresses.” — Erez Naaman
Links:
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.