Biomarker Enhanced Tissue Network Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for tissue classification in medical diagnostics are inefficient, user-dependent, and lack reproducibility, failing to effectively utilize complex information from multiple biomarker stains and often misclassify cells or handle artifacts.
Innovation Solution
The method transforms tissue image data into a biomarker enhanced tissue network (BETN), where individual cells or sub-cellular structures are nodes, and feature vectors are used to cluster and classify cells based on similarity criteria, enabling automated and reliable tissue segmentation and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis by pathologists is used, then diagnostic information can be obtained, but efficiency is low and user variability is high
Solution Approach 1:
The system enables automated self-service tissue classification by transforming tissue images into BETN representations and applying machine learning algorithms to automatically classify cells and tissue structures, eliminating the need for manual pathologist analysis while maintaining diagnostic accuracy
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an automated computational system that uses image processing, network transformation, and machine learning algorithms to perform tissue classification, thereby improving efficiency while maintaining or enhancing diagnostic precision
2Adaptability or versatility
If traditional classification techniques are used, then basic tissue types can be classified, but complex information from multiple stains cannot be utilized
Solution Approach 1:
The patent merges information from multiple biomarker stains by transforming tissue images into BETN representations that integrate data from different stains, allowing the system to utilize complex multi-stain information for comprehensive tissue classification while maintaining adaptability to various tissue types
Solution Approach 2:
The patent adds a network dimension by transforming 2D tissue images into BETN representations where nodes and edges capture complex relationships between cells and structures, enabling the system to utilize multi-stain information in a higher-dimensional space for improved classification versatility
3Measurement precision
If image segmentation is performed, then quantitative cell information can be obtained, but artifacts and out of focus images cannot be handled
Solution Approach 1:
The patent performs preliminary actions by transforming images into BETN representations and training machine learning models on diverse data including artifacts and out-of-focus images, enabling the system to recognize and correctly classify structures even in the presence of artifacts and focusing issues
Solution Approach 2:
The system incorporates feedback mechanisms through machine learning where the classifier learns from training data that includes various artifacts and image quality issues, continuously improving its ability to distinguish true biological structures from artifacts and handle out-of-focus images while maintaining quantitative accuracy
4Productivity
If automated classification is implemented, then efficiency is improved, but handling of wide variety of gland configurations and tissue types becomes difficult
Solution Approach 1:
The patent creates a universal BETN framework and machine learning classifier that can handle diverse gland configurations and tissue types through a single automated system, achieving both high classification speed and broad adaptability by learning from training data representing various tissue types and structures
Data Source
AI summary
Methods and systems for tissue classification of a tissue sample are provided. The methods and systems transform the tissue image data to a biomarker enhanced tissue network (BETN) such that an individual cell or a sub-cellular structure in the tissue image data corresponds to a node in the BETN, define a feature vector based on a feature set representative of a tissue type of interest, cluster nodes of the BETN based on a similarity criterion of one or features of the feature vector, and classify the nodes in the tissue image data based on the grouping of the nodes of the BETN.