Multi-Magnification Image Encoding for Faster Cross-System Retrieval
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Solution Overview
Problem
Existing image descriptors for digital content are time-consuming, inconsistent, and computationally intensive, limiting the efficient management and retrieval of large volumes of image data, particularly in medical imaging where multiple magnifications are required for accurate diagnosis.
Innovation Solution
A method and system for generating encoded representations of image data using artificial neural networks to process sub-images at various magnifications, extracting feature vectors, and aggregating them to create universal and adaptable descriptors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing image descriptors are used for digital content management, then image retrieval can be performed, but the process is time-consuming and computationally intensive
Solution Approach 1:
The patent segments the image processing task by generating descriptors at multiple magnification levels (e.g., 4x, 10x, 20x, 40x) independently, then combining them. This allows parallel processing of different magnification levels, significantly reducing the overall time required for descriptor generation and retrieval while maintaining comprehensive image characterization
Solution Approach 2:
The patent performs preliminary action by pre-generating and storing encoded representations at multiple magnification levels during image ingestion. When retrieval is needed, the system can quickly combine pre-computed descriptors from stored magnification levels rather than processing the entire image from scratch, thereby reducing retrieval time
2Adaptability or versatility
If existing image descriptors are used, then image management is possible, but the descriptors are inconsistent and not universal across different systems
Solution Approach 1:
The patent implements universality by creating a multi-magnification descriptor framework that can be applied across different imaging systems and modalities. The encoded representations are generated using standardized processing pipelines that produce consistent descriptors regardless of the source system, enabling universal image search and retrieval across heterogeneous platforms
Solution Approach 2:
The patent applies parameter changes by systematically varying the magnification level parameter across multiple fixed levels (4x, 10x, 20x, 40x). This standardized parameter variation ensures consistent descriptor generation across different images and systems, while the combination of multiple magnification levels provides comprehensive and reliable image characterization
3Productivity
If existing image descriptors are used, then image retrieval can be performed, but the computational intensity and storage requirements are high
Solution Approach 1:
The patent extracts only the essential features at each magnification level by using encoded representations (e.g., feature vectors from CNNs or other compression techniques) rather than storing complete multi-magnification images. This extraction reduces storage requirements while maintaining the ability to perform efficient retrieval operations
Solution Approach 2:
The patent segments the full image data into discrete magnification levels, storing only the encoded descriptors for each level rather than the complete high-resolution images. This segmentation allows the system to maintain high retrieval efficiency through multi-level descriptor comparison while dramatically reducing storage requirements compared to storing full-resolution multi-magnification image sets
Data Source
AI summary
Computer-implemented methods and systems are provided for generating encoded representations for multiple magnifications of one or more query images. An example method for involves operating a processor to obtain a query image and identify a set of anchor points within the query image. The processor is operable to generate a plurality of sub-images for a plurality of magnification levels for each anchor point. Each sub-image includes the anchor point and corresponds to a magnification level of the plurality of magnification levels. The processor is operable to, for each magnification level, apply an artificial neural network model to a group of sub-images having that magnification level to extract a feature vector representative of image characteristics of the query image at that magnification level; and to generate an encoded representation for multiple magnifications of the query image based on the feature vectors extracted for the plurality of magnification levels.