Inference Model Selection for Medical Imaging
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Solution Overview
Problem
Users face inefficiencies in selecting an appropriate inference model for medical image data from a plurality of models, requiring time and effort to determine the best model for processing, as existing techniques do not effectively match supplementary information with inference model information.
Innovation Solution
An information processing apparatus with an inference model information acquisition unit, supplementary information acquisition unit, and inference model selection unit that compares supplementary information from medical image data with information about each inference model to select the most appropriate model for processing, utilizing a database and determining matching criteria such as imaging apparatus, region, and contrast medium usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user manually selects an inference model from multiple models, then the user can choose a model for specific medical image data, but the processing efficiency is low due to the time and effort required to check and determine the appropriate model
Solution Approach 1:
The system performs automatic model selection by itself without requiring user intervention. The selection unit automatically compares supplementary information from medical image data with training data characteristics of multiple inference models and selects the most appropriate model, eliminating the need for users to manually check and determine models while maintaining high selection accuracy
Solution Approach 2:
The system pre-stores information about multiple inference models including their training data characteristics (imaging apparatus type, imaging region, contrast medium usage) in a database before actual processing. This preliminary organization of model information enables rapid automatic comparison and selection during processing without requiring users to perform preliminary analysis
2Reliability
If multiple inference models are applied to medical image data, then comprehensive inference results can be obtained, but the processing efficiency decreases because the user must check the data against each model's requirements and determine the appropriate result
Solution Approach 1:
The system automatically determines which inference model is most appropriate for the given medical image data by comparing supplementary information with pre-stored model characteristics. This self-service approach eliminates the need for users to manually check data against multiple model requirements and determine appropriate results, saving significant user time and effort while maintaining comprehensive inference capability
Solution Approach 2:
The selection unit acts as an intermediary between the medical image data and multiple inference models. It automatically compares supplementary information (imaging apparatus, region, contrast medium) with training data characteristics of multiple models and selects the most appropriate model, serving as a mediator that eliminates the need for direct user intervention in the model selection process
Data Source
AI summary
An information processing apparatus includes an inference model information acquisition unit configured to acquire information about each inference model comprising a plurality of inference models, a supplementary information acquisition unit configured to acquire supplementary information supplement to medical image data that is an inference target acquired by imaging a test subject, and an inference model selection unit configured to select an inference model to be applied to the medical image data from the plurality of inference models based on the information about each of the inference models and the supplementary information, wherein the medical image data is an inference target.