Deep Learning Stained-Cell Analysis for Faster Diagnosis
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Solution Overview
Problem
Conventional pathological diagnostic tests using glass slides are slow and time-consuming, leading to delays in diagnosis and treatment, especially with the increasing demand due to an aging population and rising cancer cases.
Innovation Solution
A method utilizing deep learning to analyze stained cells in medical images by obtaining position information and calculating staining ratios through a pre-trained neural network model, which includes generating a binary image based on staining intensity and thresholds to accurately determine the number of stained cells.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional manual examination methods are used, then diagnostic accuracy can be maintained through expert judgment, but examination speed is slow and diagnosis time is prolonged
Solution Approach 1:
The patent replaces the manual mechanical examination process with an automated deep learning-based image analysis system. The neural network model automatically detects, counts, and analyzes stained cells in medical images, eliminating the need for pathologists to manually examine each cell while maintaining diagnostic accuracy through AI-driven object detection and classification algorithms.
Solution Approach 2:
The patent creates a digital copy of the glass slide image and analyzes it through computational processing. The system generates a binary image from the original medical image, processes this digital copy through the neural network model, and derives diagnostic information without physically altering or requiring the original physical slide, thereby enabling rapid repeated analyses.
2Productivity
If deep learning-based automated analysis is implemented, then examination speed increases and diagnosis time decreases, but system complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex analysis process into distinct functional modules: image preprocessing (generating binary images from medical images), object detection (using the neural network model to identify stained cells), and quantitative analysis (counting cells and calculating staining ratios). This segmentation allows each component to be optimized independently and simplifies the overall system architecture and maintenance.
Solution Approach 2:
The patent employs a pre-trained neural network model that has been previously trained on extensive datasets of medical images. This preliminary training action is performed offline, and the trained model can then rapidly analyze new images without requiring complex real-time training computations, thereby reducing the computational complexity during actual diagnosis while maintaining high analysis speed.
3Measurement precision
If manual cell counting and staining ratio calculation are performed, then measurement precision can be controlled by expert judgment, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual measurement and calculation processes with automated computational algorithms. The system automatically calculates staining ratios by processing binary images through the neural network model, which precisely quantifies stained cell areas and computes ratios without human intervention, thereby maintaining measurement precision while eliminating the time-consuming manual measurement process.
Data Source
AI summary
Disclosed is a method for analyzing a medical image based on deep learning, which is performed by a computing device. The method may include: obtaining position information of stained cells present in a medical image by using a pre-trained neural network model; and calculating a staining ratio of the stained cells in a bounding box including the stained cells corresponding to the position information.