Dynamic Neural Architecture Selection for Unseen Medical Inputs
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Solution Overview
Problem
Existing neural networks face data inconsistencies when applied to unknown inputs, particularly in medical imaging, due to variations in scanning equipment, protocols, and human involvement, leading to degraded performance of machine learning models.
Innovation Solution
A super-network (super-net) is constructed with candidate modules in parallel to represent multi-scale appearance features, and an optimal neural network is selected for each input using unsupervised loss functions during inference, ensuring domain-specific neural networks are deployed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a static neural network is applied to unknown inputs, then the model can be deployed with fixed architecture, but data inconsistencies and degraded performance occur due to variations in scanning equipment and protocols
Solution Approach 1:
The patent implements dynamic neural network architecture selection by training a super-network that can adaptively choose the most appropriate sub-network architecture for each input based on its characteristics. This resolves the contradiction by making the network structure dynamic rather than static, allowing the system to maintain reliability across different data domains while enabling deployment.
Solution Approach 2:
The patent changes the architectural parameters of the neural network by training a super-network that selects from multiple sub-networks with different architectures. This parameter change allows the system to adapt to variations in scanning equipment and protocols, maintaining performance consistency across different medical imaging domains.
2Reliability
If domain-specific neural networks are constructed for each input type, then performance on specific domains is improved, but device complexity increases due to multiple networks
Solution Approach 1:
The patent merges multiple domain-specific sub-networks into a single super-network framework. The super-network contains all sub-networks as its architecture, allowing it to select the appropriate sub-network for each input. This combining approach maintains domain-specific performance while managing complexity through a unified selective architecture rather than separate independent networks.
Solution Approach 2:
The super-network serves as a universal framework that can handle multiple different input types and domains by selecting from its constituent sub-networks. This multi-functional design allows a single network structure to perform the work of multiple domain-specific networks, improving reliability across domains while avoiding the complexity of managing separate networks.
3Adaptability or versatility
If a super-network with candidate modules is constructed to represent multi-scale appearance features, then adaptability to unseen inputs is improved, but training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the super-network with multiple sub-networks during the training phase, so that the architecture selection capability is established beforehand. This preliminary preparation allows the network to quickly adapt to unseen inputs during inference without requiring extensive training time at deployment, as the adaptive structure is already in place.
Data Source
AI summary
Apparatuses, systems, and techniques to dynamically identify a neural network architecture for a data point. In at least one embodiment, a data point is fed to a plurality of neural network architectures, in which an optimal neural network architecture is determined based at least in part on information inferenced using the plurality of neural network architectures.