Multi-Instance Learning Classifier for Histology Tissue Analysis
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Solution Overview
Problem
Automated analysis of histological H&E tissue sections is challenging due to difficulties in cell segmentation at high magnification and subtle structural changes, requiring efficient algorithms that can handle large datasets and reduce computational costs, while traditional supervised learning methods are inefficient and labor-intensive.
Innovation Solution
A multi-instance learning framework with a utility function that dynamically selects feature sets for classification, allowing early termination of computation on negative tissues and adapting to the specific characteristics of each tissue sample, reducing computational costs and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional supervised learning methods are used with manual tracing of tumor areas, then classification accuracy can be achieved, but the process becomes labor intensive and cannot be scaled to large training sets
Solution Approach 1:
The system uses multiinstance learning where the classifier automatically learns from tissue-level labels without requiring manual annotation of individual regions. The algorithm autonomously determines which regions to analyze based on the utility function, eliminating the need for pathologist time in manual tracing while maintaining high classification accuracy.
Solution Approach 2:
The patent changes the labeling parameter from region-level manual traces to tissue-level automatic labels. This parameter change enables the system to scale to large datasets because tissue-level labels can be obtained automatically from digital pathology slides without requiring manual annotation of individual tumor regions.
2Measurement precision
If feature extraction is performed on entire tissue images at high magnification, then classification accuracy improves, but computational time increases significantly
Solution Approach 1:
The system segments the tissue image into multiple regions of interest (ROIs) and processes them independently. Instead of analyzing the entire tissue at high magnification, the algorithm identifies and analyzes only the most informative regions, significantly reducing computational time while maintaining accuracy.
Solution Approach 2:
The utility function enables the system to perform partial analysis by stopping computation early when sufficient information has been gathered to make a confident classification decision. This partial action approach avoids unnecessary computational effort on regions that do not contribute to the final diagnosis.
3Reliability
If all regions of tissue are analyzed uniformly, then comprehensive diagnosis is achieved, but computational resources are wasted on redundant information
Solution Approach 1:
The system applies different analysis strategies to different regions based on their local characteristics. The utility function evaluates each region's importance and directs computational resources to regions with the highest diagnostic value, rather than uniformly processing all regions. This local quality approach ensures reliable diagnosis while optimizing resource efficiency.
Data Source
AI summary
Systems and methods are disclosed for classifying histological tissues or specimens with two phases. In a first phase, the method includes providing off-line training using a processor during which one or more classifiers are trained based on examples, including: finding a split of features into sets of increasing computational cost, assigning a computational cost to each set; training for each set of features a classifier using training examples; training for each classifier, a utility function that scores a usefulness of extracting the next feature set for a given tissue unit using the training examples. In a second phase, the method includes applying the classifiers to an unknown tissue sample with extracting the first set of features for all tissue units; deciding for which tissue unit to extract the next set of features by finding the tissue unit for which a score: S=U−h*C is maximized, where U is a utility function, C is a cost of acquiring the feature and h is a weighting parameter; iterating until a stopping criterion is met or no more feature can be computed; and issuing a tissue-level decision based on a current state.