Classifier Error Rate Annotation for Unlabeled Data
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Solution Overview
Problem
Current methods for training classifiers, especially in medical imaging, rely heavily on manually annotated data, which is costly and inefficient, and semi-supervised methods provide lower performance compared to supervised methods.
Innovation Solution
A system that evaluates a trained classifier on labeled data to generate error rates, processes unlabeled data to create annotated data with associated error rates, and trains a second classifier using these error rates, employing forward or backward corrected losses for noise robustness and differentiability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manually annotated data is used for classifier training, then classification performance is improved, but annotation cost and time increase significantly
Solution Approach 1:
The system enables classifiers to self-correct their own errors by computing error rates on unlabeled data and using corrected loss functions to retrain, eliminating the need for manual annotation of large datasets while maintaining performance
Solution Approach 2:
The patent transforms the training approach by changing the loss function parameters to account for error rates, using corrected loss functions that incorporate noise transition matrices to adjust training weights based on predicted error probabilities
2Measurement precision
If manually annotated data is used for classifier training, then classification performance is improved, but annotation cost increases
Solution Approach 1:
The system enables classifiers to self-correct their own errors by computing error rates on unlabeled data and using corrected loss functions to retrain, eliminating the need for manual annotation of large datasets while maintaining performance
Solution Approach 2:
The patent uses computationally inexpensive error rate estimates on unlabeled data as a substitute for expensive manual annotations, treating the unlabeled data as a disposable resource that can be processed automatically without human intervention
3Ease of operation
If semi-supervised methods are used for annotation, then annotation burden is reduced, but classification performance decreases
Solution Approach 1:
The system implements feedback by computing error rates from the classifier's predictions on unlabeled data, using these error rates to compute corrected loss functions, and iteratively retraining the classifier to improve performance while maintaining low annotation burden
Solution Approach 2:
The patent performs preliminary error rate computation and loss correction on unlabeled data before final classifier training, preparing corrected training signals in advance that guide the classifier toward higher performance without requiring manual annotations
4Reliability
If error rates are computed and corrected loss functions are used, then noise robustness is improved, but computational complexity increases
Solution Approach 1:
The patent transforms the training approach by changing the loss function parameters to account for error rates, using corrected loss functions that incorporate noise transition matrices to adjust training weights based on predicted error probabilities
Solution Approach 2:
The system introduces error rate estimates and noise transition matrices as intermediary computational elements that bridge the gap between raw predictions and corrected training signals, enabling noise robustness through structured intermediate representations
Data Source
AI summary
An example system includes a processor to evaluate a trained first classifier on a test set of labeled data to generate error rates for a number of labels. The processor is to process a set of unlabeled data via the trained first classifier to generate annotated data including labels and associated error rates. The processor is to train a second classifier using the annotated data and the associated error rates.