Building AI-Ready Infrastructure for Quantitative Pathology

As AI transforms pathology from qualitative assessment to quantitative analysis, the need for robust infrastructure becomes critical. At NanoView, we're building the foundational platform that enables AI-powered image analysis, quantification, and computer-aided diagnostics.

The Infrastructure Challenge

While research labs have developed impressive AI models for pathology, deploying these tools in production requires more than just algorithms. It requires:

• High-resolution image handling: AI models need access to full-resolution slide data, not compressed thumbnails
• Standardized data formats: Consistent image formats and metadata that AI models can reliably process
• Annotation workflows: Systems for pathologists to validate and refine AI predictions
• API architecture: Easy integration points for AI models to plug into existing workflows

Our Approach: The Platform Layer

Rather than building AI models ourselves, we're creating the infrastructure layer that makes AI deployment practical. Think of us as the "operating system" for digital pathology AI.

1. High-Resolution Image Pipeline

Our platform handles high-resolution whole slide images (WSI) with efficient streaming and caching. This ensures AI models receive the full-resolution data they need without overwhelming network bandwidth or storage systems.

2. Quantification-Ready Data Structure

We're building data structures that support quantitative analysis from day one. This includes:

• Precise coordinate systems for nuclei counting and area measurement
• Metadata schemas that capture biomarker information (Ki-67, EGFR, etc.)
• Version control for annotations, allowing AI predictions to be refined over time

3. Human-in-the-Loop Workflows

Following the "AI + Pathologist > AI alone" principle, we're designing workflows where:

• AI highlights suspicious regions, pathologists confirm findings
• Quantitative measurements are suggested, but always require human validation
• Feedback loops allow pathologists to improve AI performance over time

4. API-First Architecture

Our platform exposes well-documented APIs that allow AI models to:

• Access slide images in standardized formats
• Submit predictions and receive validation feedback
• Query metadata and annotations for training data

Use Cases We're Enabling

Automated Nuclei Counting

AI models can count nuclei and measure tissue areas automatically, with pathologist validation. This saves hours of manual work while maintaining accuracy.

Biomarker Assessment

AI can assess biomarkers like Ki-67, with results integrated directly into pathology reports. The platform ensures these measurements are traceable and auditable.

Computer-Aided Diagnostics

AI highlights suspicious regions on slides, pathologists review and confirm. This collaborative workflow improves accuracy (as demonstrated by the 7 percentage point improvement with Paige) while maintaining pathologist oversight.

Molecular Property Prediction

AI models that predict mutation status from H&E images can be integrated into our platform, enabling rapid screening before expensive sequencing tests.

The Road Ahead

We're building NanoView not just as a slide viewer, but as the infrastructure layer that enables the next generation of AI-powered pathology tools. By focusing on the platform rather than the algorithms, we can partner with AI researchers and companies to bring their innovations to market faster.

The future of pathology is quantitative, AI-assisted, and collaborative. We're building the foundation that makes that future possible.