Deep Learning Algorithm for Histopathological Image Analysis
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Solution Overview
Problem
The shortage of pathologists and the inefficiency of manual feature extraction in histopathological diagnosis lead to delays in diagnosing malignant tumors, as current methods rely heavily on human observation and are labor-intensive.
Innovation Solution
An image analysis method using a deep learning algorithm to automatically generate data indicating the tumorigenic state of tissue or cell images, which includes preprocessing images, training a neural network with labeled data, and classifying pixels to distinguish between tumor and non-tumor cell nuclei.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation and feature extraction by pathologists is used, then diagnostic accuracy can be maintained through human expertise, but the workload becomes enormous and diagnosis time increases
Solution Approach 1:
The patent replaces the mechanical system of manual observation and feature extraction by pathologists with an automated image analysis system using machine learning algorithms. The system automatically extracts features from histopathological images and performs classification, substituting human manual work with computational processing while maintaining diagnostic accuracy.
Solution Approach 2:
The patent implements a self-service system where the image analysis apparatus autonomously performs feature extraction, classification, and diagnosis without requiring continuous human intervention. The machine learning model automatically learns from training data and applies the learned patterns to new images, enabling the system to serve itself in the diagnostic process.
2Productivity
If more pathologists are trained to handle the workload, then diagnostic capacity increases, but training takes an extraordinary amount of time and pathologist shortage persists
Solution Approach 1:
The patent replaces the need for extensive pathologist training with an automated system that uses machine learning algorithms. Instead of investing time in training human pathologists, the system automatically learns diagnostic patterns from training data, eliminating the time-consuming training process while increasing diagnostic capacity.
Solution Approach 2:
The patent changes the fundamental parameter of diagnostic capacity from human-dependent to system-dependent. By transitioning from manual pathologist analysis to automated image analysis with machine learning, the system achieves high diagnostic capacity without the time investment required for human training.
3Productivity
If automated image analysis is implemented, then pathologist workload decreases and diagnosis speed increases, but the system requires complex machine learning algorithms and training data processing
Solution Approach 1:
The patent segments the complex diagnostic process into distinct functional modules: image acquisition, preprocessing, feature extraction, classification, and result output. This segmentation allows each module to be independently optimized and managed, reducing the perceived complexity while maintaining high diagnostic speed.
Solution Approach 2:
The patent creates a universal image analysis platform that can handle multiple types of histopathological images and diagnostic tasks through a single machine learning system. The system is designed to be multi-functional, accommodating different image types and diagnostic requirements without requiring separate complex systems for each function.
4Measurement precision
If deep learning algorithms are used for automated analysis, then diagnostic accuracy and speed improve, but the extent of automation requires sophisticated neural network structures and training processes
Solution Approach 1:
The patent applies preliminary action by extensively training the deep learning model with labeled training data before actual diagnostic use. The system performs preliminary learning and adaptation during the training phase, so that when deployed for actual diagnosis, it can automatically and accurately analyze images without requiring complex real-time adjustments.
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from training data with known outcomes and adjusts its parameters to minimize classification errors. The training process uses feedback from comparison between predicted and actual diagnoses to continuously improve the model's accuracy, enabling high precision automation.
Data Source
AI summary
An image analysis method for generating data indicating a tumorigenic state of an image of a tissue or a cell. The image analysis method is an image analysis method for analyzing an image of a tissue or a cell using a deep learning algorithm of a neural network structure, analysis data are generated from the analysis target image including a tissue or cell to be analyzed, the analysis data are input to the deep learning algorithm, and data indicating the tumorigenic state of tissues or cells in the analysis target image are generated by the depth learning algorithm.