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Digital Pathology Image Analysis
Automate cell counting, identify regions of interest, and pre-screen slides using computer vision.
Computer visionAnatomic PathologyHospital LabsReference Labs
Manual Microscopy Bottleneck
Pathologists spend hours manually reviewing slides, counting cells, and identifying regions of interest. This is time-consuming, prone to fatigue, and creates capacity constraints.
Impact on Labs:
- •Pathologist burnout and shortage
- •Inconsistent cell counts between reviewers
- •Slow slide review turnaround
- •Capacity limitations for growth
- •Fatigue-related errors
TYPICAL COST:
$150,000-$300,000 in pathologist time annually
AI-Powered Slide Analysis
Computer vision models automatically detect cells, identify regions of interest, and pre-screen slides—allowing pathologists to focus on decision-making.
Our Approach:
- ✓Automated cell detection and counting
- ✓Region of interest highlighting
- ✓Slide quality assessment
- ✓Pre-screening for high-priority areas
- ✓Confidence scoring for pathologist review
Technology Stack:
- ◆Convolutional Neural Networks (CNN)
- ◆Object detection (YOLO, Faster R-CNN)
- ◆Image segmentation (U-Net)
- ◆Transfer learning from pre-trained models
Enhanced Pathology Workflow
✓96% cell detection accuracy
✓Process slides in <3 seconds
✓Reduce pathologist review time by 40%
✓Improve consistency
✓Enable capacity expansion
EXPECTED ROI:
12-18 month payback period
Technical Details
Model Type
CNN-based Object Detection
Performance
96% cell detection accuracy
Implementation Time
10-14 weeks for custom prototype
Data Requirements
- •Whole slide images (WSI) in standard formats
- •Annotated training images
- •Cell type classifications
- •Pathologist validation data
- •DICOM or proprietary formats supported
Interested in this use case for your lab?
Schedule a free discovery call to discuss building a custom prototype that validates this approach for your specific situation.