Histological Image Tiling for Consistent Prognostic Analysis
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Solution Overview
Problem
Existing histological image analysis methods, particularly in histopathology, suffer from inconsistency and limited prognostic value due to inter and intra-observer variability among human experts, and existing automated approaches require extensive research to identify suitable methods, making them inefficient.
Innovation Solution
A computer-implemented method using a machine-learning algorithm trained on divided tiles of histological images, with a scoring system involving double thresholding to generate a single outcome value, enabling reliable analysis and visualization of pathological features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human histopathologists manually analyze histological images, then diagnostic expertise and interpretation can be applied, but inter- and intra-observer variability causes inconsistency and limited prognostic value
Solution Approach 1:
The patent replaces the mechanical system of human expert interpretation with an automated machine learning system. The ML algorithm processes histological images through digital image analysis, eliminating the subjectivity and variability inherent in manual expert interpretation while maintaining diagnostic accuracy through trained computational models.
Solution Approach 2:
The patent creates a digital copy of the histological analysis process through machine learning models that replicate and enhance expert interpretation. The system trains on annotated image data to reproduce diagnostic decisions with greater consistency and scalability, capturing the essence of expert knowledge in a reproducible computational framework.
2Extent of automation
If conventional automated image analysis methods are used, then automation can be achieved, but extensive research and testing are required to identify suitable statistical tests and analyses
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on a comprehensive dataset of annotated histological images before actual analysis. This training phase captures the essential patterns and features in advance, allowing the system to perform automated analysis without requiring extensive real-time research or testing during deployment.
Solution Approach 2:
The patent utilizes parameter changes by training the model on diverse image data with varying staining patterns, tissue types, and pathological features. The system adapts to different imaging conditions and stain variations through parameter adjustment during training, enabling robust automated analysis across different scenarios without requiring method re-development.
3Measurement precision
If machine learning algorithms are trained on entire histological images, then comprehensive analysis can be performed, but the complexity of processing and training increases significantly
Solution Approach 1:
The patent applies segmentation by dividing the large histological images into smaller, manageable patches or regions. This segmentation allows the machine learning model to process and analyze individual regions independently, reducing the computational complexity of training and inference while maintaining the ability to perform comprehensive analysis across the entire tissue section through aggregation of regional results.
Data Source
AI summary
A machine learning algorithm is trained on a number of microscopic images and a measure of outcome of each image. Each image is divided into tiles. The measure of outcome is assigned to each tile of the image. The tiles are then used to train the machine learning algorithm. The trained algorithm may then be used to evaluate images.