Foundation Model Assessment

Foundation models are popping up everywhere – do you need one for your proprietary image dataset?

A domain-specific foundation model trained with self-supervised learning on your unique data could bring a number of benefits:

  • Less reliance on labeled data

  • Improved generalizability to domain shifts

  • The ability to use all channels of multispectral or multiplex images

  • Faster prototyping of new models

But they can be time-consuming and costly to train. And do you even have enough data to train one? How long will it take? Do you have the resources available?

What if you knew exactly what the ROI would be?

And which benefits your team is likely to see. What resources it will take to build one. Whether it is something to prioritize now versus next year. And if there is a quicker way to get started.

Get a clear perspective on whether you can benefit from a foundation model

Though this assessment, I will answer the following questions:

  • Would you benefit from a foundation model trained on your proprietary images?

  • Are there publicly available models you could try before training your own?

  • Do you have the capability to train one yourselves?

  • What are some of the factors you’d need to consider when training one?

Here’s how it works:

You’ll start by filling out a questionnaire to give me some insights into your goals, data, and relevant team members. I’ll interview some of your leadership, domain expert, and machine learning team members to discover your overall goals, the nuances of the image datasets you have available, and the capabilities of your team. With this knowledge, I’ll deliver a report with recommendations to answer the questions above. A follow up call after report delivery is also included to ensure that you have all the information you need to move forward.

Results you can expect from this assessment:

  • Save time and expenses by not pursuing a foundation model if it is unlikely to help you achieve your goals.

  • A clear understanding of how a foundation model can help you achieve your goals.

  • Recommendations on publicly available models to try before building your own.

  • Know whether your current team is capable of building a foundation model – or what expertise they’re lacking.

  • Gain some insights into the nuances and complexity of training a good foundation model so that you can better prepare for the task ahead.

Ready to get started?

Are you ready to get a clear perspective on the ROI of a foundation model for your proprietary image dataset? Apply for a Foundation Model Assessment by clicking the button below. The price is $5,000, which could save your team many months of experimentation if you choose the wrong path – and hundreds of thousands of dollars.

Don’t just take my word for it…

Heather has accelerated the development of our machine learning projects by challenging the status quo and introducing the team to new approaches. She helped us get out of the echo chamber and brought an outside perspective with very relevant alternative approaches that would have taken a lot longer to do on our own.

-- Joe Sturonas, Ancera, VP of Systems Development

Heather has deep understanding of digital pathology and machine learning and their application to whole slide images. Her in-depth review of the current state of the art research has enabled Gestalt to rapidly focus our machine learning efforts on approaches that are yielding value for our pathologists and their patients.

-- Brian Napora, Gestalt Diagnostics, VP of Sales & Product Management

Heather provided practical feedback and guidance, which allowed us to quickly take steps in the right direction and improve the performance of our models.

-- Darragh Maguire, Deciphex, Artificial Intelligence Engineer

Still have questions?

Availability is limited

I only offer two Foundation Model Assessments per month. Scheduling is first come, first served. The sooner you apply, the sooner you will have a clear perspective on foundation models.

Apply for a Foundation Model Assessment Now