Ordinal Classification Model Confidence via Binary Classifier Consistency
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Solution Overview
Problem
Current methods for determining confidence in ordinal classification models for microscope images are either computationally intensive or do not effectively account for the ordinal nature of the classification, leading to unreliable confidence measures.
Innovation Solution
A microscopy system and method that processes microscope images using an ordinal classification model, where binary classifiers provide estimates for cumulative auxiliary classes, and confidence is determined based on the consistency of these estimates, exploiting the logical order of classes without requiring excessive computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If confidence measures for ordinal classification are determined using methods suitable for regression models or normal classification models, then computational simplicity is maintained, but the ordinal character of the classification is not taken into account, leading to meaningless confidence measures
Solution Approach 1:
The patent transforms the confidence determination approach by changing the parameters used: instead of using probability output vectors from individual binary classifiers (which ignore ordinal relationships), it uses the monotonicity of classification estimates across the ordered sequence of auxiliary classes. This parameter change allows the ordinal structure to be incorporated while maintaining computational efficiency through a simple monotonicity check.
2Reliability
If multiple transformed input images are processed to determine confidence, then confidence reliability is improved, but runtime is multiplied due to multiple prediction steps
Solution Approach 1:
The patent extracts the ordinal relationship information directly from the classification estimates produced by a single model prediction, without needing to process multiple transformed images. By taking out and utilizing the monotonicity property of the classification estimates across ordered auxiliary classes, it achieves confidence determination from a single prediction step, avoiding the runtime multiplication that would result from multiple predictions.
3Measurement precision
If a plurality of model instances are used to determine confidence through variance of outputs, then confidence accuracy is improved, but memory requirements and runtime are multiplied
Solution Approach 1:
The patent enables the single ordinal classification model to self-evaluate its confidence by examining the monotonicity of its own classification estimates across the ordered auxiliary classes. This self-service approach eliminates the need for multiple model instances, thereby avoiding the multiplication of memory requirements while still providing accurate confidence measurement through the inherent ordinal structure of the problem.
Data Source
AI summary
In a computer-implemented method for determining a confidence of a calculated classification, a microscope image is processed with an ordinal classification model, which calculates a classification with respect to classes that form an order. The ordinal classification model comprises a plurality of binary classifiers which, instead of calculating classification estimates with respect to the classes, calculate classification estimates with respect to cumulative auxiliary classes, wherein the cumulative auxiliary classes differ in how many consecutive classes of the order are combined. The classification is calculated from the classification estimates of the binary classifiers. A confidence of the classification is determined based on a consistency of the classification estimates of the binary classifiers.