Neural Network Parameter Control for Adaptive Medical Imaging
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Solution Overview
Problem
Existing medical imaging apparatuses face challenges in setting appropriate parameters due to varying scanned environments, user preferences, and patient information, requiring significant development manpower and resources.
Innovation Solution
A medical imaging apparatus equipped with a neural network processor that automatically sets parameters based on image feature values, user preferences, and environmental conditions, using a self-learning module to optimize image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If parameters are manually set according to user input, then flexibility in parameter setting is improved, but the complexity of operation and time consumption increase
Solution Approach 1:
The system automatically determines optimal parameters by analyzing user preferences, environmental conditions, and device characteristics without requiring manual input. The parameter setting process serves itself by using embedded sensors and pre-stored preference data to generate appropriate settings automatically.
Solution Approach 2:
User preferences and environmental parameters are pre-stored in the system memory before actual operation. When the system operates, it retrieves and processes this pre-collected information to quickly determine optimal parameters, eliminating the need for real-time manual configuration.
2Adaptability or versatility
If multiple parameters are set to adapt to various scanned environments, then adaptability is improved, but the device complexity increases
Solution Approach 1:
The system automatically adjusts multiple parameters simultaneously based on environmental sensor data and pre-stored preferences. By changing parameters in a coordinated manner based on integrated analysis rather than individual manual configuration, the system achieves high adaptability while keeping the interface simple.
Solution Approach 2:
A single integrated processing unit handles multiple parameter types (exposure, focus, contrast, etc.) by analyzing unified input data from sensors and preference storage. This multi-functional approach allows the system to adapt to various environments without requiring separate control mechanisms for each parameter.
3Ease of operation
If automatic parameter setting is implemented, then ease of operation is improved, but the precision of parameter setting may deteriorate
Solution Approach 1:
The system incorporates feedback loops where sensor data from the actual scanning environment is continuously monitored and compared against pre-stored preferences and optimal parameter sets. This feedback mechanism allows the automatic system to refine its parameter selections and achieve high precision by learning from actual performance data.
Solution Approach 2:
Optimal parameter sets are pre-calculated and stored for various environmental conditions and user preferences through thorough analysis during system setup. When automatic mode is activated, the system retrieves these pre-optimized parameters, ensuring high precision without requiring complex real-time calculations.
Data Source
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AI summary
Provided is a medical imaging apparatus including a storage configured to store training data and an optimization coefficient; at least one processor configured to identify at least one image feature value from an input medical image, and to identify a value of at least one parameter of the medical imaging apparatus, based on the at least one image feature value and the optimization coefficient, by using a neural network processor; an output interface configured to output a resultant image generated based on the value of the at least one parameter; and an input interface configured to receive a first control input of adjusting the value of the at least one parameter, wherein the at least one processor is further configured to update the optimization coefficient by performing training using the training data and the first control input.