How we accelerate machine learning projects for pathology:
- Best practices for applying ML to pathology images
- Distill cutting-edge research for novel applications
- Clear project scope
- Smoother navigation through challenges
Machine Learning Deep Dive
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.Schedule now
Machine Learning Assessment
Do you have multiple ML projects on the go but are unsure of what to prioritize? What if you had a clear machine learning strategy for your team? My ML Assessment is an evaluation of your current ML projects to identify top areas for improvement and pinpoint any new high priority projects that will be needed to meet your overall goals. In 1-2 weeks, you’ll know where to prioritize your ML efforts.Learn more and apply
Machine Learning Project Roadmap
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.Learn more and apply
Machine Learning Domain Generalizer
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.Learn more and apply
Machine Learning Project Accelerator
$3,000 to $9,500/month
Have we recently worked together on an ML Project Roadmap or ML Domain Generalizer, and you'd like to ensure that your team stays on track? My ML Project Accelerator is a monthly ongoing service in which I guide your team as they implement the plan to build momentum and drive results. Reduce the risk of your ML project with clear next steps and fewer iterations.
To inquire about availability, send me an email.
Machine Learning Advisor
$3,000 to $15,000/month
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 monthly 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.
To inquire about availability, schedule a free Strategy Session.
Our team was fortunate to work with Heather on a short-term project, where she was a critical contributor to establishing a framework for future studies.
Pixel Scientia provides valuable insights for Gestalt's image analysis product 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.
Heather provided excellent consulting on our clinical AI research. She offered a wealth of knowledge in this domain and introduced us to new techniques to combat the failure modes that we presented. She helped to distill the latest machine learning literature relevant to our queries, and pointed us towards the most applicable and successful methods. Heather provided practical feedback and guidance, which allowed us to quickly take steps in the right direction and improve the performance of our models.
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. We can help you build the technology and your team in house, ensuring a smoother path to success.
Our 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. We will be there to help you navigate these obstacles as needed, but we consider our role most successful if we have transferred this knowledge to your team.
scene & object classification
Machine Learning:dimensionality reduction
multiple instance learning
Deep Learning:convolutional neural networks
Build an Interdisciplinary Team
We’ve worked with teams spanning many different disciplines and the best results tend to come from a combination of
- Pathologists, remote sensing scientists, 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.
We can help you in building this team. We start by setting reasonable goals for the project and determine the resources needed. From initial models, we 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.