MAML-Based Deep Learning Optimization for Medical Image Segmentation
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Solution Overview
Problem
The challenge in medical image segmentation lies in the scarcity of large datasets, the need for expert knowledge for disease classification, the cost and difficulty of annotation, and the sensitivity of patient data, which hinders effective AI-based lesion segmentation.
Innovation Solution
A deep learning model optimization method and apparatus that configures optimizer parameters independently of model parameters, performs one-shot learning after few-shot learning, and uses an average Hausdorff distance loss to reduce prediction noise, thereby improving medical image segmentation performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional deep learning models are used for medical image segmentation, then segmentation capability is provided, but segmentation performance is insufficient due to data scarcity and annotation difficulties
Solution Approach 1:
The patent changes the learning paradigm from conventional supervised learning to meta-learning (MAML), transforming the model's parameters to be more adaptable. This parameter transformation enables the model to achieve high segmentation performance with limited medical imaging data by learning optimal initialization parameters that can quickly adapt to new tasks with few shots.
Solution Approach 2:
The patent performs preliminary meta-learning training on diverse source domains to pre-adapt the model parameters before actual deployment. This preliminary action on synthetic or easily annotated data prepares the model to perform well on target domains with scarce annotated data, effectively addressing the data quantity limitation.
2Measurement precision
If expert annotation is used for training, then model accuracy is improved, but annotation cost and difficulty increase
Solution Approach 1:
The patent uses synthetic data or data from source domains as copies or proxies for the target domain data. These synthetic annotations serve as substitutes for expensive expert annotations, allowing the model to learn effective representations without requiring costly manual labeling of medical images by domain experts.
Solution Approach 2:
The meta-learning framework creates a universal model that can adapt to multiple different segmentation tasks across various medical imaging domains. This multi-functionality reduces the need for task-specific expert annotation by enabling the model to transfer knowledge from one domain to another, thereby reducing overall annotation costs.
3Measurement precision
If more training data is collected from patients, then model performance improves, but patient data sensitivity and privacy concerns increase
Solution Approach 1:
The patent introduces synthetic data or data from public source domains as an intermediary between the need for training data and patient privacy constraints. This intermediary allows the model to learn from large amounts of data without directly using sensitive patient information, thereby maintaining privacy while improving performance.
Solution Approach 2:
The patent uses synthetic or publicly available data from source domains as disposable training data that does not carry the same privacy risks as real patient data. These substitute data can be freely used for training without compromising patient confidentiality, effectively bypassing the data sensitivity issue.
4Measurement precision
If conventional optimization methods are used, then training is simple, but segmentation accuracy is insufficient
Solution Approach 1:
The patent segments the optimization process into two distinct phases: an inner loop for task-specific adaptation and an outer loop for meta-parameter updates. This segmentation of the optimization process allows each phase to focus on specific objectives, improving overall segmentation accuracy while maintaining a structured approach to the increased complexity.
Solution Approach 2:
The patent adds another dimension to the optimization process by introducing meta-parameter space alongside the conventional parameter space. This dimensional expansion allows the model to learn not just parameters but also how to optimize parameters, thereby achieving superior segmentation accuracy through hierarchical optimization.
Data Source
AI summary
Disclosed are a deep learning model optimization method and apparatus for medical image segmentation. A deep learning model optimization method for medical image segmentation includes: (a) initializing a model parameter; (b) updating the model parameter by performing model-agnostic meta learning (MAML) on a model based on sample batch and applying a gradient descent algorithm to a loss function; (c) setting an optimizer parameter as the updated model parameter, performing one-shot meta-learning on the model, and then updating the optimizer parameter by applying the gradient descent algorithm to the loss function; and (d) updating the model parameter by reflecting the updated optimizer parameter.