Medical Image Representation Search for Lower-Complexity Deep Learning
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Solution Overview
Problem
Deep learning architectures for medical image analysis face challenges with high-dimensional data, requiring large training datasets and long training times due to complex neural architectures, and the selection of suitable data representation is often based on intuition without a systematic process.
Innovation Solution
A method for optimizing the representation of medical image data by varying the content and number of representations using an optimization algorithm, combined with neural architecture search, to reduce network complexity and training data requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If high-dimensional medical image datasets are used for deep learning, then the completeness and detail of medical information is improved, but the complexity of neural architecture increases leading to longer training times and larger data requirements
Solution Approach 1:
The patent segments the high-dimensional medical image data into multiple lower-dimensional representations or views. Instead of processing the complete high-dimensional dataset directly, the system divides it into manageable components that can be processed by simpler neural architectures, thereby reducing training complexity while preserving essential medical information.
Solution Approach 2:
The patent extracts relevant features and information from the high-dimensional medical image datasets, separating essential medical characteristics from redundant data. This extraction process creates simplified representations that maintain diagnostic value while reducing the overall data dimensionality and neural network complexity requirements.
2Loss of information
If high-dimensional medical image datasets are used for deep learning, then the completeness of medical information is improved, but the amount of training data required increases
Solution Approach 1:
By segmenting high-dimensional data into multiple lower-dimensional representations, the patent reduces the amount of training data needed for each individual representation. The system can then train on several smaller datasets rather than requiring one extremely large dataset for the complete high-dimensional data.
Solution Approach 2:
The patent extracts and processes only the most relevant features from the medical image datasets, reducing the volume of training data required. This selective extraction focuses computational resources on essential medical information rather than processing all available data points.
3Loss of information
If high-dimensional medical image datasets are used for deep learning, then the completeness of medical information is improved, but the training time increases
Solution Approach 1:
The patent divides the training process into multiple stages, each handling a specific lower-dimensional representation of the medical data. This segmented approach allows for faster individual training cycles compared to training on the complete high-dimensional dataset, thereby reducing total training time while maintaining information completeness.
Solution Approach 2:
The patent performs preliminary processing and dimensionality reduction of medical image datasets before feeding them into the neural network. This preliminary action simplifies the data structure in advance, enabling faster training convergence without losing essential medical diagnostic information.
4Reliability
If data augmentation and regularization techniques are applied, then the performance of deep learning algorithms is improved, but the complexity of the system increases and requires considerable experience
Solution Approach 1:
The patent segments the complex data processing task into simpler, more manageable representation learning problems. By working with lower-dimensional representations, the system reduces the need for aggressive data augmentation and complex regularization techniques, thereby lowering system complexity while maintaining performance.
5Adaptability or versatility
If a strongly connected neural architecture with free parametrization is used, then the flexibility and adaptability of the model is improved, but the amount of training data required increases
Solution Approach 1:
The patent segments the model architecture into multiple simpler components that process different lower-dimensional representations of the data. This segmented approach maintains model flexibility and adaptability through modular design while reducing the total number of parameters that require training data, thereby lowering the training data requirement.
Data Source
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AI summary
Computer-implemented method for parametrizing a function (3), wherein the function (3) comprises a processing algorithm (5) and a representation generator (6), wherein the representation generator (6) is designed to generate at least one representation (7) based on a medical image dataset (1) and a respective representation parametrization (8, 22), and wherein the processing algorithm (5) is designed to determine at least one output parameter (4) based on the at least one representation (7) and multiple algorithm parameters (10), the method comprising using an optimization algorithm (20) to determine the processing algorithm (5) and the at least one representation parametrization (8, 22), wherein the optimization algorithm (20) optimizes a measure (27) for the performance of the processing algorithm (5) when operating on a set of training representations (7) generated by applying the representation generator (6) to training medical image datasets by varying on the one hand the content of the at least one representation parametrization (8, 22) and/or the number of used representation parametrizations (8, 22) and on the other hand the processing algorithm (5) and the algorithm parameters (10).