Foundation Model Series: Enabling Digital Pathology Workflows with Dmitry Nechaev from HistAI
What happens when you combine AI with digital pathology? In this episode, Dmitry Nechaev, Chief AI Scientist and co-founder of HistAI, joins me to discuss the complexity of building foundation models specifically for digital pathology. Dmitry has a strong background in machine learning and experience in high-resolution image analysis. At HistAI, he leads the development of cutting-edge AI models tailored for pathology.
HistAI, a digital pathology company, focuses on developing AI-driven solutions that assist pathologists in analyzing complex tissue samples faster and more accurately. In our conversation, we unpack the development and application of foundation models for digital pathology. Dmitry explains why conventional models trained on natural images often struggle with pathology data and how HistAI’s models address this gap. Learn about the technical challenges of training these models and the steps for managing massive datasets, selecting the correct training methods, and optimizing for high-speed performance. Join me and explore how AI is transforming digital pathology workflows with Dmitry Nechaev!
Key Points:
- Background about Dmitry, his path to HistAI, and his role at the company.
- What whole slide images are and the challenges of working with them.
- How AI can streamline diagnostics and reduce the workload for pathologists.
- Why foundation models are a core component of HistAI’s technology.
- The scale of data and compute power required to build foundation models.
- Outline of the different approaches to building a foundation model.
- Privacy aspects of building models based on medical data.
- Challenges Dmitry has faced developing HistAI’s foundation model.
- Hear what makes HistAI’s foundation model different from other models.
- Learn about his approach to benchmarking and improving a model.
- Explore how foundation models are leveraged in HistAI’s technology.
- The future of foundation models and his lessons from developing them.
- Final takeaways and how to access HistAI’s open-source models.
Quotes:
“Regular foundation models are trained on natural images and I'd say they are not good at generalizing to pathological data.” — Dmitry Nechaev
“In short, [a foundational model] requires a lot of data and a lot of [compute power].” — Dmitry Nechaev
“Public benchmarks [are] a really good thing.” — Dmitry Nechaev
“Our foundation models are fully open-source. We don't really try to sell them. In a sense, they are kind of useless by themselves, since you need to train something on top of them, so we don't try to profit from these models.” — Dmitry Nechaev
“The best lesson is that you need quality data to get a quality model.” — Dmitry Nechaev
“[HistAI] don't want AI technologies to be a privilege of the richest countries. We want that to be available around the world.” — Dmitry Nechaev
Links:
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