Empower your research and healthcare delivery with the latest pathology AI technologies utilizing local, low-cost hardware and secure, cloud-based infrastructure.
Our well-validated software brings the latest foundation models and cutting-edge methods, enabling you to build & deploy digital biomarkers for molecular target identification, risk stratification, and treatment response prediction.
With enterprise-grade tools built on an open-source foundation, we offer unmatched commercial performance to build & deploy powerful models that leverage research-friendly flexibility, allowing seamless transition from exploration to production deployment.
Our technology platform uses Generative AI to explain the pathological features associated with your biomarker. Understanding how and why your model works can guard against confounding & bias and drive scientific discovery.
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Our patent-pending uncertainty quantification methods help you build healthcare-ready predictive models that abstain on unfamiliar data.
We have a growing library of published biomarkers ready for research use and prospective clinical validation.
A biomarker capable of performing histologic subtyping and molecular classification in a group of rare pediatric tumors.
Siddhi Ramesh, MD
npj Precision Oncology, 2024
A multi-modal biomarker capable of identifying patients with early-stage breast cancer who are at increased risk of relapse after surgery.
Fred Howard, MD
npj Breast Cancer, 2023
A novel technique for explaining what deep learning pathology models learn during training, facilitating model transparency and driving scientific discovery.
James Dolezal, MD
npj Precision Oncology, 2023
Our patent-pending uncertainty quantification methods help you build healthcare-ready predictive models that abstain on unfamiliar data.
James Dolezal, MD
Nature Communications, 2022
Our comprehensive description of the problems with batch effects and confounders on multi-institution data, which can cause biased models and put patients at risk.
Fred Howard, MD
Nature Communications, 2021
Description of our thyroid cancer biomarker, which identifies a tumor's molecular signature and predicted invasiveness from its histopathological appearance.
James Dolezal, MD
Modern Pathology, 2020
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