Automated Neural Network Selection for Medical Image Segmentation
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Solution Overview
Problem
Current approaches to lesion segmentation in medical imaging require manual design of network architectures and training strategies, leading to sub-optimal solutions and segmentation accuracy, especially in tasks like lesion segmentation in medical imaging.
Innovation Solution
An automated deep learning method that uses a transformer-based approach to select and optimize neural network architectures and configurations for specific tasks, such as lesion segmentation, by training a relational predictor to compare performance between different configurations and selecting the most accurate ones for image segmentation tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual design of network architectures and configurations is used, then human expertise and control are maintained, but segmentation accuracy and optimization performance deteriorate
Solution Approach 1:
The system employs automated machine learning algorithms that self-optimize neural network architectures and configurations without requiring manual human intervention. The algorithm automatically determines optimal network structures, hyperparameters, and training strategies, enabling the system to serve itself in the design process while achieving superior segmentation accuracy compared to manual approaches.
Solution Approach 2:
The invention systematically varies and optimizes multiple parameters including network architecture configurations, hyperparameters, and training strategies through automated search and evaluation. This comprehensive parameter optimization enables the system to identify the most effective configurations for specific segmentation tasks, thereby improving accuracy while eliminating manual parameter tuning complexity.
2Productivity
If manual determination of network parameters is performed, then human judgment is applied, but solution optimality and training efficiency worsen
Solution Approach 1:
The system implements automated feedback loops where the machine learning algorithm continuously evaluates candidate neural network configurations through validation performance metrics. Based on this feedback, the algorithm iteratively refines and selects optimal architectures and hyperparameters, ensuring solution optimality while accelerating the training process through systematic exploration rather than manual trial-and-error.
Solution Approach 2:
The invention performs preliminary automated optimization of network architectures and configurations before actual training begins. By pre-determining optimal network structures and hyperparameters through automated search and validation, the system eliminates time-consuming manual design iterations and ensures that training starts with already-optimized configurations, thereby improving both efficiency and optimality.
3Extent of automation
If automated methods are used to select neural networks, then manual intervention is reduced, but computational resources and processing time increase
Solution Approach 1:
The system applies partial automation by selectively optimizing only the most critical aspects of neural network design (architecture and key hyperparameters) while leaving less important parameters to default values or simpler tuning. This targeted approach achieves high automation benefits in terms of accuracy improvement while limiting excessive computational resource consumption that would result from optimizing every possible parameter.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to select neural networks. In at least one embodiment, one or more first neural networks can be used to select one or more second neural networks, as may be based at least in part upon an inference to be generated by the one or more second neural networks.