AI powered tissue slide interpretation
Built computational models to analyze staining for biomarkers such as HER2, Trop2, CD8, and H&E. The outputs support high accuracy scoring and help detect and characterize cancer subtypes.
Overview
SequoiaAT collaborated with a global healthcare innovator to build a digital pathology platform that supports precise tissue slide analysis for oncology. The system uses modern image analysis to quantify biomarkers that inform treatment decisions.
We partner with life sciences teams across leading hubs including San Diego, Boston, New Jersey, and Oxford to speed up digital diagnostics and biomarker programs.
Key contributions
Interactive digital slide viewer
Delivered a fast viewer that lets pathologists zoom, annotate, and score samples in real time for detailed visual assessment and clinical precision.
Automation using Python and Playwright
Automated key steps in the pipeline, including slide scoring and validation. This improved consistency, boosted turnaround speed, and reduced human error in routine assessments.
Comprehensive quality assurance
Owned the QA lifecycle from requirements to test design, automation, and release sign off. Every release met strict standards for performance, usability, and reliability.
Business impact
- Faster diagnostic turnaround. Automation and algorithmic scoring reduced time spent per case.
- Improved clinical insight. Enhanced biomarker analysis supports precise and personalized treatment strategies.
- Reliability in production. A robust QA framework lowered defect rates and built trust in clinical settings.
Ongoing innovation
The platform continues to evolve with new AI features, gains in algorithmic precision, and usability improvements. Future versions target predictive analytics and deep learning for the next wave of digital diagnostics.
Explore our Life Sciences work, our AI and ML capabilities, and how we approach digital transformation for regulated industries.