Deep Learning Tissue Image Analysis for Reproducible Layer Evaluation
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Solution Overview
Problem
The evaluation of tissue layer structures in medical diagnostics, such as atrophy and hyperplasia of epithelial cell layers, is subjective and varies among pathologists due to visual inspection, making reproducibility difficult.
Innovation Solution
An image analysis method using a deep convolutional neural network learning algorithm to generate data indicating the layer structure in tissue images, allowing for quantitative evaluation and reducing variability based on pathologist skill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If visual inspection by pathologists is used to evaluate tissue layer structures, then diagnostic flexibility and adaptability are maintained, but measurement precision and reproducibility deteriorate due to subjective variations among pathologists
Solution Approach 1:
The patent replaces the manual visual inspection method (mechanical observation) with an automated image processing system using deep learning algorithms. The system processes histological images through convolutional neural networks to automatically identify and measure tissue layer structures, eliminating subjective human variation while maintaining diagnostic capability.
Solution Approach 2:
The patent introduces an intermediate computational layer between the raw histological image and the final diagnostic conclusion. This intermediary system uses trained deep learning models to extract quantitative features from images, serving as an objective mediator that translates visual information into measurable data without direct human intervention in the measurement process.
2Measurement precision
If automated image processing is used to evaluate tissue layer structures, then measurement precision and reproducibility improve, but device complexity and algorithm manufacturing difficulty increase
Solution Approach 1:
The patent performs preliminary actions by pre-training deep learning algorithms on large datasets of annotated histological images before deployment. This advance preparation creates ready-to-use models that can be directly applied to new images without requiring complex real-time training, simplifying the manufacturing and deployment process while maintaining high measurement precision.
Solution Approach 2:
The patent utilizes parameter changes in the form of adjusting algorithmic hyperparameters and processing thresholds to optimize performance for different tissue types and diagnostic requirements. By systematically tuning these parameters during development, the system achieves high precision across various applications without requiring fundamentally different algorithms for each case.
3Productivity
If deep learning algorithms are used for tissue analysis, then productivity and analysis speed improve, but loss of information may occur due to automated processing
Solution Approach 1:
The patent implements feedback mechanisms where the deep learning system's outputs are validated against ground truth data during training and can be reviewed or adjusted by pathologists during deployment. This feedback loop ensures that automated analysis maintains diagnostic information completeness by continuously refining its interpretations based on expert verification.
Solution Approach 2:
The patent applies segmentation by dividing the complex tissue analysis task into multiple specialized deep learning models, each trained to detect specific tissue layer structures or features. This modular approach allows the system to process different aspects of the image independently and combine results, maintaining comprehensive diagnostic information while achieving high throughput through parallel processing.
Data Source
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AI summary
An image analysis method for analyzing an image of a tissue collected from a subject using a deep learning algorithm of a neural network structure. The image analysis method comprises generating analysis data from the analysis target image that includes the tissue to be analyzed, inputting the analysis data to a deep learning algorithm, and generating data indicating a layer structure configuring a tissue in the analysis target image by the deep learning algorithm.