X-ray Diagnostic Apparatus Model Adaptation Check
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Solution Overview
Problem
Machine learning-based X-ray diagnostic models struggle to perform effectively when input images fall outside their training data scope, leading to unintended processing results and potential erroneous diagnoses, as the adaptation of these models to new images is not detectable.
Innovation Solution
An X-ray diagnostic apparatus that determines whether a trained model is adapted to an input image and applies image processing to ensure adaptation, using a processing circuitry that calculates the deviation of image features from training data statistics to perform necessary preprocessing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a trained model is used for X-ray image processing, then excellent performance and desired image processing results are achieved when the input image falls within the training data scope, but unintended image processing results and erroneous diagnoses occur when the input image falls outside the training data scope
Solution Approach 1:
The system performs preliminary evaluation of the input X-ray image to determine whether it falls within the training data scope before the trained model processes the image. This preliminary action prevents the model from processing images it cannot handle, thereby avoiding unintended results while maintaining excellent performance for suitable images.
Solution Approach 2:
An image evaluation unit acts as an intermediary between the input image and the trained model. This intermediary evaluates the image characteristics and determines adaptability, serving as a bridge that prevents mismatched images from reaching the model while allowing suitable images to proceed for high-quality processing.
2Productivity
If the trained model is applied to X-ray images without adaptability assessment, then processing efficiency is maintained, but the user cannot know whether the model would exhibit its performance
Solution Approach 1:
The system implements feedback by evaluating the input image and providing information about model adaptability to the user. This feedback mechanism informs users whether the trained model is suitable for the given image, preventing盲目 processing while maintaining efficient workflow for appropriate images.
Solution Approach 2:
The image evaluation unit enables the system to self-assess whether an input image is suitable for the trained model without requiring external judgment. This self-service capability automatically determines model adaptability, preserving processing efficiency while providing necessary information about model performance reliability.
3Speed
If image processing is performed without evaluating model adaptability, then processing speed is maintained, but erroneous diagnoses and unnecessary radiation exposure may occur
Solution Approach 1:
The system takes preliminary anti-action by evaluating image adaptability before processing, thereby preventing harmful outcomes such as erroneous diagnoses and unnecessary radiation exposure. This preliminary check blocks unsuitable images from entering the processing pipeline, counteracting potential harm before it can occur while maintaining speed for suitable images.
Solution Approach 2:
The adaptability evaluation is performed as a preliminary action before the trained model processes the X-ray image. This preliminary step prevents erroneous diagnoses and unnecessary radiation exposure by identifying unsuitable images before they undergo processing, thereby eliminating harmful effects while preserving processing speed for appropriate cases.
Data Source
AI summary
According to one embodiment, an X-ray diagnostic apparatus includes processing circuitry. The processing circuitry is configured to acquire an X-ray image. The processing circuitry is configured to perform a determination as to whether or not a first trained model, to which the X-ray image is to be input, is adapted to the X-ray image, and if the determination indicates that the first trained model is not adapted to the X-ray image, apply image processing to the X-ray image so that the first trained model serves as an adapted model.