Customization Layer for Imaging Device Data Normalization
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Solution Overview
Problem
Conventional systems for controlling imaging devices face challenges due to differences in normalization of training and test data, leading to reduced performance, especially in situations with noise, missing features, or nonlinear adjustments, as they struggle to account for variances that are difficult to detect or describe.
Innovation Solution
The implementation of an additional customization layer, comprising an artificial neural network, which is trained to normalize data specific to the differences between subsets of data, allowing for precise processing and output for imaging devices, including magnetic resonance imaging applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a universal artificial neural network is trained on all subsets of training data with different normalizations, then the system can handle multiple data sources, but the precision of object detection decreases compared to training on a single normalized subset
Solution Approach 1:
The system segments the training process into two distinct stages: first training a universal artificial neural network on all data subsets to establish baseline object detection capabilities, then training separate customization layers on each specific data subset to refine performance. This segmentation allows the system to maintain versatility across multiple data sources while preserving high precision through specialized customization layers for each subset.
Solution Approach 2:
The customization layers are nested within the universal artificial neural network architecture. Each customization layer is integrated as a specialized component that processes specific data subsets while leveraging the foundational capabilities of the universal network. This nested structure enables the system to combine the adaptability of the universal network with the precision of subset-specific customization.
2Adaptability or versatility
If training data from multiple subsets with different normalizations is used, then more comprehensive coverage is achieved, but the system performance deteriorates due to normalization inconsistencies
Solution Approach 1:
Customization layers serve as intermediary components between the universal artificial neural network and specific data subsets with different normalizations. These intermediary layers adapt the normalized input from each specific subset to match the expectations of the universal network, thereby maintaining performance consistency across diverse data sources without requiring retraining of the entire system.
Solution Approach 2:
The customization layers learn and apply parameter transformations specific to each data subset's normalization characteristics. By dynamically adjusting parameters such as scaling factors, offset values, and normalization constants based on the input data subset, the system maintains reliable performance across varying normalization schemes while comprehensively covering multiple data sources.
3Adaptability or versatility
If conventional systems process images with different normalizations, then multiple data sources can be utilized, but detection accuracy decreases due to undetected normalization differences
Solution Approach 1:
The customization layers automatically detect and adapt to the normalization characteristics of each input data subset without requiring manual intervention or explicit metadata about the data source. The system performs self-service normalization adaptation by learning the statistical properties of each subset and applying appropriate transformations, thereby maintaining high detection accuracy across multiple data sources while preserving versatility.
Data Source
AI summary
Methods and systems are described for autonomous control of imaging devices. In particular, the methods and system described herein may account for the differences in normalization of training data and/or test data. The methods and systems may process images through an additional customization layer, which itself may comprise an artificial neural network. The additional customization layer is trained to normalize data for specific applications and/or differences between subsets of data.