Impact AI Foundation Models for Pathology with Razik Yousfi from Paige
Foundation models have been at the forefront of AI discussions for a while now and joining me today on Impact AI is a leader in the creation of foundation models for pathology, Senior Vice President of Technology at Paige AI, Razik Yousfi. Tuning in, you’ll hear all about Razik’s incredible background leading him to Paige, what the company does and how it’s revolutionizing cancer care, and the role machine learning plays in pathology. Razik goes on to explain what foundation models are, why they are so helpful, how to train one, the differences in training one for pathology specifically, and how they use foundation models at Paige AI. We then delve into the challenges associated with the creation of foundation models before my guest shares some advice for leaders in machine learning. Finally, Razik tells us where he sees Paige AI in the next few years.
Key Points:
- Introducing today’s guest, Razik Yousfi.
- An overview of Razik’s background and what led him to become Senior Vice President of Technology at Paige AI.
- What Paige does and why it’s important for cancer care.
- The role machine learning plays in pathology.
- Razik tells us what a foundation model is, why it’s useful, and what it takes to train one.
- The subtle differences in training a foundation model for pathology versus other data.
- How they are using foundation models at Paige AI.
- Razik discusses what the future of foundation models for pathology looks like.
- Why Razik doesn’t suggest that every organization build a foundation model.
- Our guest shares some advice for leaders of machine learning teams.
- Where he sees the impact of Paige AI in the next three to five years.
Quotes:
“Paige is focusing on digital and computational pathology. In other words, we really bring AI and novel AI solutions to the field of pathology to help pathologists make better-informed decisions.” — Razik Yousfi
“A foundational model is a model trained on a very large set of data. The idea there is that you can, in turn, use that foundation model to build a wide range of downstream applications.” — Razik Yousfi
“Building a foundation model is not easy. So, I wouldn't necessarily recommend to every organization to build a foundation model.” — Razik Yousfi
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.