Case Study: Genmab speeds up digital pathology tumor detection with V7 Darwin

How Genmab Uses V7 to Speed Up Tumor Detection in Digital Pathology Images

Genmab, a biotechnology company developing antibody therapeutics for cancer treatment, needed a way to annotate complex digital pathology images with speed and precision. Its teams of pathologists and machine learning engineers were working to improve tumor classification and cancer diagnosis, but the manual review process required robust tooling, tight collaboration, and strong quality control. Genmab used V7 Darwin for digital pathology image annotation and QA.

With V7, Genmab built streamlined annotation and review workflows, onboarded pathologists more quickly, and labeled more than 5,000 pathology images for training and validation data. V7’s API, intuitive interface, multi-level QA, and dataset management features helped the team label tiled image sections, filter and score images, fix errors on the go, and speed up model development. The result was a more efficient workflow and faster progress toward higher-accuracy deep learning models, with V7 making it easier for Genmab to get data into a format ready for ML.

Genmab

David Soong

Director, Translational Data Science