3D Segmentation Training Using Bayesian Pseudo-Label Filtering
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Solution Overview
Problem
Existing machine-learning based 3D segmentation models require extensive labeled datasets, which are difficult to obtain, and semi-supervised training methods suffer from confirmation bias and unreliable uncertainty estimators, limiting their performance.
Innovation Solution
A semi-supervised training method using Bayesian statistics to estimate uncertainty of pseudo-labels generated by a pre-trained model, filtering out unreliable labels through entropy-based thresholding and unanimous voting, allowing training on both labeled and unlabeled datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If self-training methods use pseudo-labels from a pre-trained model to train on unlabeled datasets, then the model can be trained with more data, but confirmation bias limits performance
Solution Approach 1:
The patent introduces an uncertainty estimator as an intermediary component that evaluates the reliability of pseudo-labels before they are used for training. This mediator filters out low-confidence predictions, preventing confirmation bias from degrading model performance while still allowing the incorporation of abundant unlabeled data.
Solution Approach 2:
The system implements a feedback mechanism where the uncertainty estimator continuously evaluates pseudo-label quality and provides feedback to the training process. High-uncertainty pseudo-labels are excluded from training, creating a feedback loop that maintains performance reliability while leveraging large datasets.
2Reliability
If thresholding schemes filter pseudo-labels based on confidence level, then confirmation bias is reduced, but uncertainty estimators using softmax function lack reliability
Solution Approach 1:
The patent changes the parameter used for uncertainty estimation from softmax confidence scores to entropy-based measurements. This parameter transformation provides a more reliable and calibrated uncertainty estimate, enabling effective thresholding that accurately identifies high-quality pseudo-labels without the limitations of softmax-based approaches.
Solution Approach 2:
The system replaces the conventional softmax-based uncertainty estimation mechanism with an entropy-based estimation approach. This substitution provides more accurate and reliable uncertainty measurements, improving the effectiveness of pseudo-label filtering while maintaining computational efficiency.
3Reliability
If extensive labeled datasets are used for training segmentation models, then model performance is improved, but labeled datasets cannot be easily obtained
Solution Approach 1:
The patent implements a self-service approach where the model generates its own training data through pseudo-labeling of unlabeled datasets. The uncertainty estimator enables the system to self-evaluate and select high-quality pseudo-labels, creating a self-sufficient training pipeline that reduces dependence on manually labeled data while maintaining performance.
Solution Approach 2:
The system performs preliminary action by pre-training the model on available labeled data to establish initial competence. This preliminary training enables the model to generate reliable pseudo-labels for unlabeled data, creating a bootstrapping effect that expands training data availability without requiring extensive manual labeling from the outset.
Data Source
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AI summary
A computer-implemented method for training a segmentation model is provided. The method comprises: (S10) pre-training a segmentation model under supervised learning using a labeled dataset; (S20) generating ensembles of pseudo-labels associated to elements of an unlabeled dataset using stochastic forward passes with the pre-trained segmentation model; (S30) estimating the uncertainty of the pseudo-labels for each element of the unlabeled dataset using Bayesian statistics over the ensembles of pseudo-labels; (S40) identifying reliable elements in the unlabeled dataset, the reliable elements being characterized by having pseudo-labels with an estimated uncertainty that is inferior to a predetermined threshold; and (S50) training the segmentation model using a new training dataset comprising the labeled dataset and the reliable elements with associated pseudo-labels.