Cell Morphology Classification for Disease Discernment
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Solution Overview
Problem
Existing methods for disease analysis, such as JP H10-197522 A, focus on tissue images but do not effectively utilize individual cell images for disease discernment.
Innovation Solution
A computer-implemented method that classifies the morphology of individual analysis target cells from a specimen, using deep learning algorithms and machine learning techniques to analyze cell morphology classification information and discern diseases based on individual cell images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If tissue image analysis methods are used for disease discernment, then existing analysis can be performed, but individual cell level disease discernment capability is insufficient
Solution Approach 1:
The patent segments the tissue image analysis into two distinct levels: (1) tissue-level analysis that examines overall tissue architecture and patterns, and (2) individual cell-level analysis that extracts features from each cell independently. This segmentation allows the system to leverage both approaches, achieving precise disease discernment by combining tissue context with detailed cellular morphology information.
2Measurement precision
If individual cell image analysis is implemented, then disease discernment precision is improved, but analysis complexity increases
Solution Approach 1:
The analysis system is segmented into modular components: a tissue analysis module for overall tissue assessment, a cell extraction module for isolating individual cells, a feature extraction module for capturing cellular characteristics, and a classification module for disease discernment. This modular segmentation reduces system complexity by allowing each component to be developed and optimized independently.
Solution Approach 2:
The patent introduces an intermediary feature extraction layer that transforms raw individual cell images into standardized feature vectors. This intermediary representation serves as a bridge between complex image data and the classification algorithm, simplifying the overall analysis process while preserving critical diagnostic information.
3Loss of information
If individual cell morphology classification is performed, then cell level diagnostic information is obtained, but processing time increases
Solution Approach 1:
The patent extracts only the most diagnostically relevant features from individual cell images, such as nuclear morphology, cytoplasmic characteristics, and cellular arrangement patterns. By selectively extracting key features rather than processing complete high-resolution images, the system retains essential cell morphology information while significantly reducing processing time.
Solution Approach 2:
The system performs partial analysis on individual cells by focusing on specific morphological features that are most indicative of disease states, rather than comprehensively analyzing all aspects of each cell. This partial action approach maintains diagnostic accuracy for hematopoietic system diseases while reducing overall processing time.
Data Source
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AI summary
Disclosed is a method for supporting disease analysis, the method including: classifying, on the basis of images obtained from a plurality of analysis target cells contained in a specimen collected from a subject, a morphology of each analysis target cell, and obtaining cell morphology classification information corresponding to the specimen, on the basis of a result of the classification; and analyzing a disease of the subject by means of a computer algorithm, on the basis of the cell morphology classification information.