Accelerating machine learning projects for pathology startups

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Are you struggling with the challenges of machine learning projects?

  • Complex and iterative projects
  • Uncertain outcomes
  • Rapidly advancing field
  • Elusive ML talent
  • Lack of ML expertise in pathology

Meet Heather D. Couture, PhD
Consultant & Researcher

I leverage my expertise to unlock the value in your data, create more accurate models, and generate new powerful insights from pathology images.

Featured in:

Scientific American
The pathologist
Digital Pathology Association
IEEE Spectrum

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The Power of Machine Learning

Automate labor-intensive
or mundane processes
Increase efficiency
and repeatability

  • Count mitoses
  • Segment glands
  • Characterize nuclei shape
Improve precision
and productivity

  • Find tumor in whole slide image
  • Predict one imaging modality or stain from another
Innovate to learn concepts
beyond human capabilities
Discover new insights
and drive impact

  • Distinguish classes too complex for human experts
  • Infer molecular biomarkers from H&E
  • Predict patient outcome

Organizations I've Worked With

Digital Smiths

Get the most out of your images

  • Define clear goals and metrics of success
  • Properly preprocess data for effective model training
  • Iterate quickly to build momentum
  • Get from a sufficient to an ideal solution to maximize impact

Schedule a free Machine Learning Strategy Session

Ensure the Success of Your Project

Like you, I care about driving impact. I can help you navigate this confusing AI journey. With 15 years of computer vision and machine learning experience, I’ve seen models that fail for a particular task and those that succeed. I have many tools in my toolbox and the research experience to create novel algorithms for unique situations.

My specialty is problems for which there is no existing packaged solution. I make use of today’s most powerful machine learning tools - TensorFlow, Keras, PyTorch, sklearn, and others - to help you create a new solution based on images and any other available data.

In collaborating with pathologists, geneticists, epidemiologists, and biostatisticians, I’ve learned a great deal of the common terminology to effectively communicate across domains.

I have been working on pathology applications for more than a decade and have created methods to predict cancer biomarkers too complex for pathologists to see.

Together, we can generate new insights from your project too.

Success Stories

Machine learning projects for pathology have unique challenges

Large & Diverse Images

Whole slide histopathology images are often more than 60,000 pixels across. They contain multiple tissue types and both tumor and non-tumor tissue. Tissue appearance is heterogeneous, both from patient to patient and sometimes within a single tumor.

Weak Labels

For some applications like mitosis detection and tissue segmentation, pathologists can provide detailed annotations. But for patient-level prediction tasks like molecular biomarkers, treatment response, or patient outcomes, the algorithm must learn itself which regions of the image are important, often employing multiple instance learning.

Limited Labeled Data

Some applications of computer vision make use of millions of labeled images, whereas data sets of 1000 or so patients are much more common for medical applications. Transfer learning and self-supervised methods are often critical to success in this low sample size regime.

Additional Modalities of Data

The most powerful models that can improve patient care and outcomes often use multiple modalities of data - histopathology, clinical, genomic, proteomic, etc. Specialized models can make use of these structured and unstructured sources of data.

Language Barrier Between Disciplines

Understanding the intricacies of a particular application and possible clinical use cases requires the expertise of pathologists and other domain experts. Project success depends on communicating both ways - about the disease and about the machine learning solution.

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The Challenges of Real World Pathology Data

The process for machine learning is empirical and iterative - hypothesize a model, test it, and improve it.

While many talented machine learning engineers can create and train a model, they may be inexperienced with the challenges posed by real world data. The complexities of massive images and noisy or missing labels are a whole different ball game than clean benchmark data sets.

Machine learning engineers may also lack the experience to identify unique aspects of data that, with a customized model, can improve predictions.

I spent my Ph.D. developing solutions to study breast cancer and learned to create new machine learning methodologies motivated by particular aspects of the research data.

The same may be beneficial for your project, but you won’t know it without looking from the right perspective.