Multiview Polyp Re-Identification for Faster Endoscopic Follow-Up
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Solution Overview
Problem
Endoscopic procedures face inefficiencies due to the loss of visibility and low confidence in re-identifying previously detected polyps, leading to wasted clinical time and potential missed polyp management.
Innovation Solution
An endoscopic system utilizing a machine-learned model with a transformer architecture for re-identifying polyps by fusing multi-image inputs, employing self-supervised contrastive learning to enhance polyp re-identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual polyp re-identification is performed during endoscopic procedures, then the endoscopist can locate previously detected polyps, but valuable clinical time is lost due to searching and low confidence in re-identification
Solution Approach 1:
The patent replaces the manual mechanical search process with an automated computer vision system using transformer-based deep learning models. The system automatically processes endoscopic images to detect and re-identify polyps, substituting the endoscopist's manual visual search with algorithmic image analysis, thereby eliminating time loss while maintaining high identification accuracy
Solution Approach 2:
The system performs preliminary polyp detection and marking during the initial examination phase. By pre-identifying and flagging polyps before the procedure continues, the system prepares re-identification data in advance, allowing rapid retrieval and verification without time-consuming manual search during subsequent phases of the procedure
2Reliability
If manual polyp re-identification is performed, then the endoscopist can assess previously detected polyps, but false re-identification may result in missing polyps that would have been managed
Solution Approach 1:
The transformer-based system incorporates feedback mechanisms where detection results are continuously refined through self-supervised contrastive learning. The model learns from positive pairs (same polyp across different images) and negative pairs (different polyps), continuously improving its discrimination capability and reducing false re-identification while increasing reliability
Solution Approach 2:
The patent introduces an intermediary computer vision system between the endoscopist and the polyp detection task. This intermediary layer processes images through multiple transformer encoders and contrastive learning modules to generate reliable re-identification results, filtering out false positives before they reach the clinical decision-making process
3Measurement precision
If multi-view input images are processed using traditional methods, then polyp detection can be performed, but the system lacks the accuracy needed for reliable re-identification
Solution Approach 1:
The patent transitions from traditional single-view or simple multi-view image processing to a sophisticated multi-dimensional approach using transformer architectures. The system processes images across multiple dimensions (spatial, temporal, and feature spaces) through self-supervised contrastive learning, achieving high re-identification accuracy by leveraging dimensional complexity rather than avoiding it
Solution Approach 2:
The system employs a composite architecture combining multiple transformer encoders, projection layers, and contrastive learning modules. This composite structure integrates different functional components (image encoding, feature extraction, similarity computation) into a unified system that achieves superior re-identification performance despite the inherent complexity
Data Source
AI summary
An endoscopic system, methods, and a machine-learned model trained to represent an area of interest in a body, such as a polyp, are described. In an embodiment, the machine-learned model uses data comprising multiple images of the area of interest as a vector in a latent space. In an embodiment, the methods include comparing a first plurality of images of a portion of a body and a second plurality of images of a portion of the body to determine a likelihood that the first portion is the second portion.