Pathology Image Recognition with YOLOv5s and Cell Type Scoring
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Solution Overview
Problem
Existing digital pathological diagnosis methods suffer from subjective variability and inefficiency due to the need for manual observation of pathology slides, and existing deep learning models face instability and inconsistency due to random initial background vectors, affecting accuracy and effectiveness.
Innovation Solution
A method and system utilizing deep learning for pathology image recognition involving image segmentation, preprocessing, and feature extraction using YOLOv5s network, followed by a comprehensive evaluation coefficient to determine cell types, including perimeter, area, shape factor, and color analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If manual observation of pathology slides is used for diagnosis, then diagnosis can be performed with traditional methods, but the workload is large and diagnosis results are prone to subjective differences
Solution Approach 1:
The patent replaces the mechanical manual observation system with an automated deep learning-based image recognition system. The convolutional neural network automatically analyzes pathology images to identify cell types and characteristics, eliminating human subjectivity and providing consistent, objective diagnosis results while reducing the workload on pathologists.
Solution Approach 2:
The patent creates a digital copy of the pathology slide imaging system that can be repeatedly analyzed by the deep learning model. This digital copy allows for multiple analyses of the same image data without the variability introduced by manual re-examination, ensuring consistent and reproducible diagnosis results.
2Productivity
If digital pathological diagnosis is used to improve efficiency, then storage and reading form is changed, but diagnosis results are still affected by subjective factors and time consumption remains high
Solution Approach 1:
The patent substitutes the manual continuous observation process with an automated deep learning system that processes digital pathology images rapidly. The convolutional neural network analyzes entire slides or regions of interest automatically, dramatically reducing the time required compared to traditional manual scanning and observation methods.
Solution Approach 2:
The patent performs preliminary image segmentation and feature extraction before final diagnosis. By pre-processing the images to identify and isolate specific cell types and characteristics, the system prepares the data in advance for rapid analysis, reducing the overall time required for diagnosis while maintaining high accuracy.
3Adaptability or versatility
If random initial background vector is generated in deep learning model, then feature extraction can be performed, but model stability and consistency are affected and accuracy decreases
Solution Approach 1:
The patent changes the parameter initialization approach by replacing random initialization with a specific initialization method that sets the initial background vector to zero or uses predefined values. This parameter change ensures consistent and stable model performance across different runs, eliminating the variability introduced by random initialization while maintaining the model's ability to extract meaningful features.
Data Source
AI summary
An automatic recognition method includes the following steps: collecting multiple digital pathology slide images as sample data, segmenting collected sample image data, where segmented cell images includes a positive cell image and a negative cell image, and the positive cell image and the negative cell image obtained by segmentation are stored into a positive cell image set and a negative cell image set correspondingly; preprocessing images in the two image sets to facilitate subsequent recognition and extraction of a single cell picture in the image; acquiring the extracted single cell picture, extracting a feature of a single cell image to be recognized, and training an initial neural network with a corresponding cell feature as a label; and generating a comprehensive evaluation coefficient according to the cell feature corresponding to the image cell in each region, and determining a detailed cell type according to the comprehensive evaluation coefficient.