Fight Cancer With AI

Guiding R&D teams to a more successful machine learning solution



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The Power of Machine Learning




  • Count mitoses
  • Segment glands
  • Characterize nuclei shape



  • Find tumor in whole slide image
  • Predict one imaging modality or stain from another



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

Featured Pathology Projects

Breast Cancer Subtypes

Image analysis with deep learning was used to distinguish histologic and molecular properties of tumors from H&E.

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Retina OCT Segmentation

We developed a solution for segmenting layers of the human retina from noisy OCT images.

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Imaging + Genomics

Histologic image features and genomics provide two complementary views of tumors. By combining the two, a more complete picture of tumor prognosis and treatment models is possible.

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Are You Looking to Create New Imaging Biomarkers and Struggling with Machine Learning?

Expectations Exceed Reality

The current hype in AI and precision medicine leads many projects to set an unreasonable target or unclear success criteria leading them to over promise and under deliver.

Experience Deficit

Machine learning appears accessible because of the availability of many open source toolkits. But once you get started, it can be difficult to decipher why a particular model isn’t working, leading to wasted time on unsuccessful approaches.

Data Dependencies

The challenge is that there is no one-size-fits-all solution. The best approach often takes a lot of experimentation, and the most efficient path is dependent on understanding your data.

Data Complexities

Applications in pathology are often challenged by the limited availability of training data, lack of detailed annotations, gigapixel images, and additional modalities of data. Accommodating all these complexities into a single solution requires advanced research techniques.

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.

There should be an easier path to driving impact with machine learning.

Ensure the success of your project with a machine learning expert at your side.

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

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 8 years and have created methods to predict cancer biomarkers too complex for pathologists to see.

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


Heather D. Couture
Consultant & Researcher
About me

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.

Is Deep Learning the Best Approach?

Deep learning is a game changer for many applications. The power of deep learning comes from its ability to find patterns in complex data - even patterns beyond the limits of human perception. It is a new way to gain insights from data. Similar to how we learn from experience, deep learning performs a task repeatedly, tweaking how it does it each time to improve the outcome.

But there are also many situations - like limited training data or interpretability requirements - in which more traditional machine learning might be best. I am well-versed in both methodologies and will help you strategize as your project, your data, and your goals evolve.