Services

How I accelerate the path to generalizable computer vision models for pathology startups:

  • Handle the challenges of real world pathology data
  • Create a clear project roadmap
  • Design generalizable models
  • Properly validate models
  • Smoothly navigate through challenges
  • Build an interdisciplinary team

Machine Learning Strategy Options

Advisor

$15,000 to $33,000/quarter

Are you struggling to keep your machine learning projects on the path to success? What if you had a clear ML strategy for your team? My Machine Learning Advisor is a quarterly ongoing service to support your team across multiple projects. I’ll keep you abreast of the latest research for your application, provide feedback on algorithm development, hiring advice, and more. Get access to ML insights from an experienced researcher when you need them.

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Project Roadmap

$24,000

Are you afraid your machine learning project will turn into an endless stream of failed experiments? What if you had a clear strategy for implementation? My ML Project Roadmap is a strategic plan outlining the components you’ll need to make your ML project a success using best practices for applying ML to pathology images and bringing in cutting-edge research where needed. In 3-4 weeks, you’ll have a clear plan for your ML project.

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Domain Generalizer

$15,000

Does your machine learning model perform well on your training data but fail on images from a different source? What if you could train models that generalize to different labs and scanners? My ML Domain Generalizer is a customized plan for improving model generalizability. In 3 weeks, you’ll have a clear plan for getting your project back on track.

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Domain Shift Assessment

$6,000

Are you confident that your machine learning models will generalize to images from different labs and scanners? What if you had a clear strategy for validating your models and improving their generalizability? My ML Domain Shift Assessment provides recommendations to give your team a clear perspective on the variations, batch effects, and biases affecting your machine learning models. You’ll learn how to properly validate your models and how to approach each type of variation.

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Deep Dive

$750

Are you facing a specific machine learning challenge? Do you have a pressing question? What if you could talk to an expert quickly? Schedule a 1 hour ML Deep Dive now. Ask me anything about whatever challenges you’re facing. I’ll give you no-nonsense advice that you can put into action immediately.

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Schedule a free Machine Learning Strategy Session to discuss which service is best for you

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Don’t Outsource, Insource - and Empower Your Team

While machine learning certainly can bring new insights, precision, and efficiency, it takes time to build the technology to do it. And the path to a successful solution is generally not linear.

Many companies outsource machine learning projects. While this can be successful for well-defined applications with a limited scope, high impact projects are different. These projects are often part of a company’s core technology, so they prefer to keep the intellectual property development in house. But they may not have navigated the unique complexities of a machine learning project before. I can help you build the technology and your team in house, ensuring a smoother path to success.

My goal is not just to get you to an ideal solution but to ensure your team also understands how it works so that they can modify it and adapt to new challenges that arise. I will be there to help you navigate these obstacles as needed, but I consider my role most successful if I have transferred this knowledge to your team.

Build an Interdisciplinary Team

I’ve worked with teams spanning many different disciplines and the best results tend to come from a combination of

  • Pathologists or other domain experts who understand the data and how it is collected,
  • Data scientists who gather and analyze it further,
  • Machine learning engineers who train and analyze models, and
  • Software engineers who bring all the pieces together and create a robust system.

Due to the interdisciplinary nature of teams, communication is key.

I can help you in building this team. We start by setting reasonable goals for the project and determine the resources needed. From initial models, I then assess possible next steps including gathering and labeling more data, cleaning data, extending models, and improving computational efficiency. More resources (human or computer) may be needed as the project progresses.