Today, I am joined by Maximilian Alber, Co-founder and CTO of Aignostics, to talk about pathology for precision medicine. You’ll learn about Aignostics’s mission, how they are impacting healthcare, and the transformative power of foundational models. Max explains how Aignostics is driven by the belief that machine learning and data science will help improve healthcare before expanding on the role of foundational models. He describes how they built their foundational model, what sets it apart from other models, and why diversity in their datasets is key. He also breaks down how foundational models have allowed them to develop other models more quickly and better navigate explainability with concepts that are challenging for machine learning. We wrap up with Max’s advice for leaders of other AI-powered startups and where he expects Aignostics will be in the next five years. Tune in now to learn all about foundational models and the innovative work being done at Aignostics!

Key Points:

• Insight into Max’s role at Aignostics and how the company is impacting healthcare.
• How they use machine learning to set themselves apart from their competitors.
• A rundown of their models and datasets.
• The definition of a foundation model and how Aignostics built theirs.
• How to use foundation models as a starting point for building machine learning applications.
• What sets Aignostics’ foundation model for histopathology apart from other similar models.
• How their foundation model enables them to develop other models more quickly.
• Top lessons Max has learned from developing foundation models.
• How they navigate explainability with concepts that are challenging for machine learning.
• The positive impact that foundational models have had on explainability.
• Recent advancements that Max is excited about as potential use cases for Aignostics.
• Max’s advice to leaders of other AI-powered startups.
• The impact of Aignostics and where he expects it will be in the next three to five years.

Quotes:

“Our mission is to turn biomedical data into insights.” — Maximilian Alber

“Everything we do is driven by the belief that machine learning and data science will help us improve healthcare.” — Maximilian Alber

“A foundation model is a model that can be used as a starting point for building a machine learning application, with the promise that the foundation model already has a great understanding of the domain.” — Maximilian Alber

“We are in active discussions for licensing our foundation model to other companies in order to enable their development as well. [What’s] important here is that we develop our foundation model along regulatory requirements, which will allow it to be used in medical products.” — Maximilian Alber

“One needs to build a technology that either makes a difference in the long run, or one must be able to innovate at a very fast pace.” — Maximilian Alber

Links:

Maximilian Alber on LinkedIn

Aignostics

Aignostics on LinkedIn

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.

Advanced weather forecasts are the new frontier in meteorology. Long-term forecasting has garnered significant attention due to its potential to provide valuable insights to various sectors of society and the economy. In today’s episode, Sam Levang, Chief Scientist at Salient, joins me to discuss Salient’s innovative approach to weather forecasting. Salient specializes in providing highly accurate subseasonal-to-seasonal weather forecasts ranging from 2 to 52 weeks in advance.

In our conversation, we discuss the ins and outs of the company’s innovative approach to weather forecasting. We delve into the hurdles of subseasonal-to-seasonal forecasting, how machine learning is replacing traditional weather modeling approaches, and the various inputs it uses. Discover the value of machine learning for post-processing of data, the type of data the company utilizes, and why it uses probabilistic models in its approach. Gain insights into how Salient is catering to the impacts of climate change in its weather predictions, the company’s approach to validation, how AI has made it all possible, and much more!

Key Points:

• Sam's background in science and the creation of Salient.
• Hear how Salient is revolutionizing weather forecasting and why.
• How Salient is utilizing machine learning in its forecasting models.
• Examples of the data and models the company uses.
• The challenges of working with weather data to build models.
• Explore why Salient also uses probabilistic models in its approach.
• Salient’s approach to validation and how it deals with data uncertainty.
• Ways AI has made the company’s approach to forecasting possible.
• He shares advice for leaders of other AI-powered startups.

Quotes:

“Salient produces weather forecasts that extend further into the future than most people are used to seeing. We go up to a year in advance.” — Sam Levang

“ML (Machine Learning) models have proved to be actually a very effective replacement for the traditional approach to weather modeling.” — Sam Levang

“The only difference about making forecasts longer timescales of weeks and months ahead is that there are some differences in the particular parts of the climate system that provide the most predictability.” — Sam Levang

“While ML and AI are extremely powerful tools, they are still just tools and there's so much else that goes into building a really valuable product, or a service, or a company.” — Sam Levang

Links:

Sam Levang on LinkedIn

Salient

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.

Welcome to today’s episode of Impact AI, where we dive into the groundbreaking world of virtual tissue staining with Yair Rivenson, the co-founder and CEO of PictorLabs, a digital pathology company advancing AI-powered virtual staining technology to revolutionize histopathology and accelerate clinical research to improve patient outcomes. You’ll find out how machine learning is used to translate unstained tissue autofluorescence into diagnostic-ready images, gain insight into overcoming AI hallucinations and the rigorous validation processes behind virtual staining models, and discover how PictorLabs navigates challenges like large files and bandwidth dependency while seamlessly integrating technology into clinical workflows. Yair also provides invaluable advice for AI-powered startup leaders, emphasizing the importance of automation and data quality. To gain deeper insights into the transformative potential of virtual tissue staining, tune in today!

Key Points:

• The origin story of PictorLabs and the research that informed it.
• Why Pictor’s work is so important for patients and the healthcare system.
• What Yair means when he says machine learning is the “engine” for virtual staining.
• How Pictor mitigates the challenge of AI hallucinations.
• Insight into what goes into validating virtual staining models.
• Large files, bandwidth dependency, and other challenges that Pictor faces.
• A look at how this technology fits smoothly into the clinical workflow.
• Collaborating with economic partners while staying focused on business objectives.
• Yair’s product-focused advice for leaders of AI-powered startups
• What the next three to five years looks like for PictorLabs.

Quotes:

“The most important factor for the healthcare system, for the patient is the fact that you can get all the results, all the workup, and all the different stains from a single tissue section very, very fast.” — Yair Rivenson

“Machine learning is the engine behind virtual staining. In a sense, that’s what takes those images from the autofluorescence of the unstained tissue section and converts [them] into a stain that pathologists can use for their diagnostics.” — Yair Rivenson

“At the end of the day, the network is as good as the data that it learns from.” — Yair Rivenson

“The more you automate, the better off you’ll be in the long run.” — Yair Rivenson

Links:

Yair Rivenson

PictorLabs

PictorLabs on LinkedIn

‘Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning’

‘Assessment of AI Computational H&E Staining Versus Chemical H&E Staining For Primary Diagnosis in Lymphomas’

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.