Patch Grid Annotation Quality Control for Medical Images
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Solution Overview
Problem
The large size and dimensionality of medical images pose challenges for effective data curation and annotation, leading to issues like missing data, noise annotation, and lack of real-time feedback, which affect the quality and performance of deep learning models.
Innovation Solution
A method involving overlaying a patch grid on images to receive and analyze annotation information, providing quality control and ensuring high-quality training data by optimizing patch generation, using techniques like region selection, patch size determination, and real-time feedback mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If medical images are divided into smaller patches for annotation and processing, then processing speed and annotation efficiency are improved, but data quality deteriorates due to missing data and noise annotation
Solution Approach 1:
The patent divides large medical images into smaller patches for annotation and processing. This segmentation enables parallel processing and makes annotation more manageable, directly improving productivity while maintaining data quality through quality control mechanisms
Solution Approach 2:
The patent implements real-time feedback mechanisms that provide quality control information during the annotation process. This feedback loop allows annotators to correct issues immediately, preventing noise annotation and maintaining high data quality despite the segmented approach
2Reliability
If complete annotation of all patches is performed, then training data quality is improved, but annotation time and resource consumption increase
Solution Approach 1:
The patent applies quality control thresholds that determine when sufficient annotation has been achieved. Rather than requiring complete annotation of all patches to the same level, the system identifies when quality standards are met, reducing unnecessary annotation time while maintaining training data quality
Solution Approach 2:
The patent applies different quality control standards to different regions and patches based on their importance. Critical regions receive more rigorous quality control, while less critical areas use streamlined processes, optimizing the balance between annotation time and training data quality
Data Source
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AI summary
A method for processing image data using an analysis model, comprising the steps of: receiving image data comprising an image; overlaying a patch grid overlay on the image, the patch grid overlay comprising at least one patch overlaying the image; receiving annotation information for the one or more patches overlaying the image; and analysing the annotation information to provide annotation quality information.