Convolutional Neural Network Tissue Image Region of Interest Determination
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Solution Overview
Problem
Current digital image analysis techniques face challenges in accurately determining regions of interest in tissue images, particularly in distinguishing between different tissue types and identifying specific features like tumors, due to variance in visual features and relationships with other tissue types, which hinders effective diagnosis and treatment decisions.
Innovation Solution
A system utilizing a convolutional neural network with a convolutional classification layer is employed to partition tissue images into areas, identify tissue types, and determine regions of interest by matching descriptions based on features such as size, density, shape, and spatial distribution, enabling accurate classification and visualization of tumor types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning classifiers are used to analyze tissue images, then the analysis can be performed, but the accuracy is reduced due to variance in visual features and relationships with other tissue types
Solution Approach 1:
The tissue image is partitioned into multiple discrete areas, and each area is independently classified into a tissue type. This segmentation approach allows the system to handle variance in visual features by analyzing smaller, more homogeneous regions separately, thereby improving overall classification accuracy and reliability.
Solution Approach 2:
The classifier is configured to determine regions of interest based on tissue types of specific areas rather than treating the entire image uniformly. This local quality approach enables the system to account for spatial variations in tissue characteristics and relationships with adjacent tissue types, improving the precision of region of interest determination.
2Difficulty of detecting and measuring
If a convolutional neural network with multiple convolutional layers is used, then feature detection capability is improved, but the complexity of the system increases
Solution Approach 1:
The convolutional neural network is structured with multiple convolutional layers that progressively divide and analyze features at different levels of abstraction. Each layer segment handles specific feature detection tasks, improving the overall difficulty of detecting and measuring complex tissue features while managing system complexity through modular layer design.
Solution Approach 2:
The network transforms the input image through multiple convolutional layers, adding dimensional transformations in the feature space. This allows the system to detect complex features by projecting them into higher-dimensional feature maps, improving feature detection capability while the layered structure manages computational complexity.
3Measurement precision
If manual analysis by experts is performed, then nuanced determination can be achieved, but the time and productivity are reduced
Solution Approach 1:
The system performs automated classification of tissue areas and determination of regions of interest without requiring manual expert analysis. The classifier independently processes the partitioned areas, uses tissue type information to identify regions of interest, and generates results automatically, thereby maintaining nuanced determination accuracy while significantly improving analysis productivity.
Solution Approach 2:
The patent replaces manual expert analysis (mechanical human cognition) with an automated computer-based classification system. The classifier uses algorithmic processing to achieve nuanced determination of regions of interest, substituting human expertise with machine learning that operates faster while maintaining or improving accuracy.
Data Source
AI summary
A method of determining a region of interest in an image of tissue of an individual by an apparatus including processing circuitry may include executing, by the processing circuitry, instructions that cause the apparatus to partition an image of tissue of an individual into a set of areas, identify a tissue type of each area of the image, and apply a classifier to the image to determine a region of interest, the classifier being configured to determine regions of interest based on the tissue types of the set of areas of the image.