Our histopathology foundation model demonstrates leading performance on public benchmarks and not only improves the accuracy of downstream tasks, but also dramatically reduces the need for training data.

Explainable AI uses layer-wise relevance propagation to infer which model inputs had a significant impact on AI model results (e.g.,  which image/data features correlate with patient outcomes).

Label Extraction increases the accuracy and scalability of traditional annotation approaches through the use of same section stainings that are co-registered with pixel-level precision and leveraged for automated cell-level annotation of the underlying image. 

Our browser-based client portal enables integrated viewing and co-registration of mIF, IHC, and H&E, quickly rendering millions of cells in an intuitive, fully interactive manner.