Path Foundation Model

Path Foundation is a machine learning (ML) model that produces embeddings based on digital pathology images. The embeddings can be used to efficiently build AI models for pathology analysis-related tasks, requiring less data and less compute than having to fully train a model without the embeddings or the pretrained model.

Trained on large scale datasets, Path Foundation helps businesses and institutions in healthcare and life sciences do more with less pathology data, accelerating their ability to build AI models for pathology image analysis.

For details about how to use the model and how it was trained, see the Path Foundation model card.

Common Use Cases

The following sections present some common use cases for the model. You're free to pursue any use case, as long as it adheres to the Health AI Developer Foundations terms of use.

Data-efficient classification

Path Foundation can be used for data-efficient classification tasks, including:

  • Identifying tumor tissue and other distinct tissue classes
  • Grading tumors
  • Identifying the presence of known biomarkers to predict treatment response
  • Exploration of pathology features for novel biomarker discovery
  • Any variety of other feature detection and classification tasks performed on Whole Slide Images (WSIs)
  • Detecting clinical features within tissues
  • Determining the type of tissue or type of stain
  • Determining pathology image quality

With a small amount of labelled data, you can train a classifier model on top of Path Foundation embeddings. Furthermore, the embedding from each tissue patch only needs to be generated once and can be used as an input for a variety of different classifiers, with very little additional compute.

For an example of how to use the model to train classifiers using the CAMELYON16 Dataset, see the following Colab notebooks:

Path Foundation can also be used to find similar images within or even between WSIs. By selecting reference patches of interest, you can use the embeddings from Path Foundation to quantify similarity between any other set of patches based on distances in the Path Foundation embedding space. This lets you identify and retrieve patches most similar to your reference regions.

Next Steps