Research Paper

Core Innovation

This paper introduces PolypVision, a three-stage hierarchical framework combining classification and segmentation with task-specific loss functions and transfer learning. It integrates multiple polyp classification tasks and segmentation into a unified pipeline, achieving state-of-the-art accuracy and device independence without hardware-specific tuning.

Why It Matters

Colorectal cancer is a leading cause of mortality, often developing from precancerous polyps. Accurate and timely polyp analysis improves early intervention and patient outcomes. PolypVision's device-independent, automated approach streamlines workflows and supports clinicians across varied imaging systems, enabling scalable adoption.

Potential Customers & Pain Points

• Hospitals – Need accurate and fast polyp diagnosis
• Endoscopy centers – Require device-independent AI tools
• Medical researchers – Need reliable polyp classification data
• Healthcare providers – Seek to reduce colorectal cancer mortality through early detection

Business Model

Freemium web application with tiered subscription plans for advanced features and enterprise integration; potential partnerships with medical device manufacturers and healthcare providers.

Competitive Landscape

• Medtronic GI Genius
• Olympus EndoAI
• Fujifilm CAD EYE

Implementation Challenges

• Regulatory approval for clinical use
• Integration with diverse hospital IT systems
• Clinician trust and adoption of AI tools

Validation Strategy

• Conduct multi-center clinical trials to validate diagnostic accuracy and workflow impact
• Obtain regulatory clearances (FDA
• CE) for clinical deployment
• Pilot deployments in endoscopy centers to gather real-world usage data and feedback