Automated Tumor Grading via Snippet Analysis
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Solution Overview
Problem
Current methods for diagnosing glioblastoma tumors are inefficient, as they rely heavily on human expertise and can lead to inconsistent diagnoses due to the difficulty in identifying certain tumor features, particularly for inexperienced doctors, and require extensive time from senior doctors.
Innovation Solution
A system for grading tumors that includes an image obtaining module, a snippet obtaining module, an analyzing module, and an outputting module, which uses trained detection models to identify classification features from pathological images, allowing for automatic tumor identification and reducing reliance on human experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual diagnosis methods are used by doctors, then diagnostic accuracy can be maintained through human expertise, but diagnostic efficiency is low and requires extensive time
Solution Approach 1:
The patent replaces the manual mechanical diagnosis process with an automated computer-based image analysis system. The system uses algorithms to automatically detect and analyze tumor features in pathological images, substituting the doctor's manual observation and measurement processes. This automation dramatically improves diagnostic efficiency while reducing the time required for analysis.
Solution Approach 2:
The diagnostic system performs self-analysis by automatically processing pathological images without requiring continuous human intervention. The computer-based system independently identifies tumor features, measures parameters, and generates diagnostic results, enabling the diagnosis process to serve itself rather than relying entirely on manual doctor review.
2Measurement precision
If manual tumor feature identification is performed, then diagnostic accuracy can be achieved, but inconsistency occurs between different doctors and biopsy samples
Solution Approach 1:
The patent implements a universal computer-based analysis system that applies the same algorithms and criteria to all pathological images regardless of which doctor would review them. This universal approach ensures consistent measurement and evaluation standards across different cases and different practitioners, eliminating variability in diagnostic criteria while maintaining high accuracy through standardized feature detection and analysis.
3Measurement precision
If detailed analysis of each image is performed to identify tumor features, then diagnostic accuracy is improved, but the process becomes excessively time-consuming
Solution Approach 1:
The system performs preliminary automated analysis of pathological images, pre-identifying tumor features and measuring key parameters before final diagnostic review. By conducting the time-consuming detailed analysis automatically in advance, the system prepares comprehensive data that can be quickly reviewed and confirmed, thereby maintaining high measurement precision while significantly improving overall diagnosis speed.
Data Source
AI summary
Method and system for grading a tumor. For example, a system for grading a tumor comprising: an image obtaining module configured to obtain a pathological image of a tissue to be examined; a snippet obtaining module configured to obtain one or more snippets having one or more sizes from the pathological image; an analyzing module configured to obtain one or more classification features based on at least analyzing the one or more snippets using one or more selected trained detection models of the analyzing module, wherein each selected trained detection model is configured to identify one or more classification features; and an outputting module configured to determine a tumor identification result based on at least the one or more classification features and output the tumor identification result.