Patch-Based Cancer Cell Classification via Feature Density Routing
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Solution Overview
Problem
Existing image analysis techniques for pathology applications, such as whole slide imaging, do not always produce accurate categorization of cancer cells, leading to unreliable diagnostic results.
Innovation Solution
A computer-implemented method for automated mask generation in image analysis, which involves receiving a digital image, segmenting it into patches, generating feature vectors, determining feature density, selecting a classifier based on feature density ranges, applying the classifier to generate classifications, compiling these classifications, and generating a mask for the image based on the overall classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis techniques are used for whole slide imaging, then the analysis process is simple, but the categorization accuracy of cancer cells is insufficient
Solution Approach 1:
The image analysis process is segmented into multiple stages: patch extraction from whole slide images, feature vector generation from patches, and classification at both patch and slide levels. This segmentation allows complex analysis to be broken down into manageable components, improving accuracy without overwhelming system complexity
Solution Approach 2:
Different classification approaches are applied to different regions based on feature density. Patches with varying feature densities are routed to appropriate classifiers, allowing the system to adapt its analysis strategy to local image characteristics, thereby improving overall categorization accuracy
2Reliability
If a single classifier is used for all patches, then the system is simple to implement, but the diagnostic accuracy is reduced
Solution Approach 1:
The system changes the parameter of classifier selection based on feature density. A stack of classifiers is maintained, each optimized for specific feature density ranges. The feature density parameter determines which classifier from the stack is applied to each patch, improving diagnostic accuracy through parameter-based adaptation
Solution Approach 2:
The classification system is made dynamic by selecting classifiers based on real-time feature density assessment. Rather than using a static single classifier for all patches, the system dynamically chooses the most appropriate classifier from the stack based on the characteristics of each patch, enhancing reliability
3Measurement precision
If feature density-based classifier selection is implemented, then categorization accuracy improves, but computational complexity increases
Solution Approach 1:
Feature density is calculated for each patch before classifier selection, serving as a preliminary assessment that guides the subsequent classification step. This preliminary action allows the system to efficiently route patches to appropriate classifiers without requiring complex real-time adjustments during classification
Solution Approach 2:
Feature density acts as an intermediary parameter between image processing and classification. Rather than directly applying complex classification logic to all patches, the system uses feature density as an intermediate metric to select from a stack of classifiers, reducing overall computational complexity while maintaining accuracy
Data Source
AI summary
A method at a computing device for classifying elements within an input, the method including breaking the input into a plurality of patches; for each patch: creating a vector output; applying a characterization map to select a classification bin from a plurality of classification bins; and utilizing the selected classification bin to classify the vector output to create a classified output; and compiling the classified output from each patch.