About
MobiDev developed an AI PoC in digital pathology using deep learning and computer vision. The project focused on processing complex Whole Slide Images (WSIs) up to 20 GB each. By implementing advanced semantic segmentation and classification models, the solution automated tissue analysis, providing high-fidelity cellular insights while overcoming heavy computational burdens.
Challenge, approach, and impact
Managing Extreme Data Volume and Lack of Ground Truth
The client required a deep learning partner to engineer a pathology PoC utilizing high-resolution Whole Slide Images (WSIs), which often exceed 20 GB per file in TIFF or SVS formats. The key technical challenge lay in optimizing computer vision architectures to process these massive domains without crashing memory limits. Additionally, the team had to establish a data ingestion pipeline from public archives while maintaining strict anonymity and research privacy.
How we built
Testimonials
Anonymous
“Working with Whole Slide Images is a true stress test for any computer vision engineer because a single file can be tens of gigabytes. We bypassed hardware bottlenecks by building an intelligent patch-based processing pipeline that feeds data to our segmentation models efficiently. What really won the client over, though, was our emphasis on explainable AI. Pathologists won't trust a black box; by building occlusion and activation maps directly into our introspection layer, we gave medical professionals the power to see exactly why the AI flagged a specific cellular structure.“
Team structure
Client team
Nicole
Data Science Lead
The client stakeholders were working closely with the team at MobiDev
Agency team
AI Engineer
Production
Project Manager
Production
