ML Model Predicting Semantic Context for Contrast-Enhanced Medical Imaging
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Solution Overview
Problem
Conventional methods for detecting and segmenting regions of interest (ROIs) in contrast-enhanced medical images are time-consuming, prone to errors, and lack standardization due to variability in image appearance across different phases and clinical protocols, requiring extensive expert annotations and struggling with slow convergence of 3D models.
Innovation Solution
A two-step approach where a pre-trained machine-learning model predicts time-related information, enabling unsupervised training and reducing the need for expert annotations, with the further model built upon this pre-trained model to predict semantic context information, leveraging pharmacokinetics of contrast agents to extract phase-dependent features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If supervised learning methods are used to train ML models on all available phases, then the model can learn from comprehensive data, but the high variability in ROI appearance across phases makes it difficult for the model to converge
Solution Approach 1:
The patent segments the training process into two distinct phases: unsupervised pre-training on individual phases followed by supervised fine-tuning on multi-phase data. This segmentation allows the model to first learn phase-specific characteristics without the confounding variability of multiple phases, then integrate multi-phase information in a controlled manner, resolving the convergence issue while retaining comprehensive training data benefits
Solution Approach 2:
The patent applies preliminary unsupervised pre-training to initialize the model with phase-specific features before performing supervised fine-tuning. This preliminary action prepares the model by establishing stable phase-specific representations, which then serve as a foundation for learning from multi-phase data, enabling convergence despite the variability across phases
2Measurement precision
If 3D models are used to improve detection and segmentation by exploiting volumetric information, then detection accuracy is improved, but the large number of parameters causes slow convergence during training and slow inference speed
Solution Approach 1:
The patent applies preliminary unsupervised pre-training to initialize the 3D model with phase-specific features before supervised fine-tuning. This preliminary action reduces the effective number of parameters that need to be learned during supervised training by pre-establishing phase-specific representations, thereby accelerating convergence while maintaining the model's 3D volumetric capabilities and detection accuracy
Solution Approach 2:
The patent extracts and processes individual phases separately during the unsupervised pre-training stage, learning phase-specific features independently. This extraction approach allows the model to capture volumetric information from each phase without the computational burden of processing all phases simultaneously, reducing training time while preserving the benefits of 3D volumetric analysis
3Measurement precision
If conventional approaches rely on a large number of expert annotations of training images, then the reference standard for training is improved, but the process becomes time consuming and expensive to acquire
Solution Approach 1:
The patent implements self-service through unsupervised pre-training, where the model learns phase-specific features automatically from the data without requiring expert annotations. The model serves itself by identifying and learning from phase characteristics inherent in the imaging data, eliminating the need for time-consuming and expensive manual annotation while still achieving high-quality training
Solution Approach 2:
The patent performs preliminary unsupervised pre-training that automatically extracts phase-specific features without expert intervention. This preliminary action creates a foundation of learned features that can then be refined with minimal annotations during supervised fine-tuning, dramatically reducing the annotation burden while maintaining reference standard quality
Data Source
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AI summary
A machine-learning model is pre-trained in an unsupervised manner to predict time-related information based on data obtained from a contrast-enhanced medical imaging measurement. This pre-trained machine-learning model is then used to build another machine-learning model to predict semantic context information for images determined from the contrast-enhanced medical imaging measurement.