Diagnosis Support Classifier Selection for Preference-Based Detection
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Solution Overview
Problem
Existing diagnosis support systems fail to adequately reflect user preferences in sensitivity settings and detection results, as they do not account for individual user needs when switching between different classifiers or models, and users cannot grasp how detection results change with updates.
Innovation Solution
A diagnosis support system that displays performance information of multiple classifiers side by side, allowing users to select the most suitable classifier based on their preferences, and outputs detection results accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple classifiers with different performances are used, then detection accuracy and user preference alignment are improved, but device complexity increases
Solution Approach 1:
The system segments the classification function into multiple independent classifiers, each optimized for different detection priorities (e.g., high sensitivity vs. high specificity). This allows the system to handle complexity by dividing it into manageable, specialized components rather than one monolithic classifier.
Solution Approach 2:
The diagnosis support system is designed to be multi-functional by incorporating multiple classifiers that can be selected based on different user preferences and clinical scenarios. The system universally handles various detection needs through a single integrated platform that supports classifier switching.
2Measurement precision
If classifiers are updated to improve performance, then detection results improve, but users cannot grasp how detection results change
Solution Approach 1:
The system implements feedback by displaying performance information of different classifiers to users. This allows users to see how detection results may vary between classifiers and understand the implications of switching between them, thereby preventing information loss about detection result changes.
Solution Approach 2:
The system performs preliminary action by displaying performance information before the user selects a classifier. This allows users to anticipate how detection results will change based on their selection, rather than discovering changes after the fact.
3Ease of operation
If sensitivity settings are standardized, then system operation is simplified, but individual user preferences cannot be reflected
Solution Approach 1:
The system transitions from static, standardized sensitivity settings to dynamic, user-selectable classifiers. Each classifier embodies different sensitivity characteristics, and users can dynamically switch between them based on their preferences and clinical needs, making the system both easy to operate and highly adaptable.
Solution Approach 2:
The system allows parameter changes by enabling users to select different classifiers with varying sensitivity and specificity parameters. This maintains ease of operation through a simple selection interface while accommodating individual user preferences through parameter diversity across classifiers.
Data Source
AI summary
A diagnosis support system includes a processor. The processor is connected to a plurality of classifiers that are different in performance. The processor displays performance information of each of the classifiers side by side, receives a user's selection of the performance information displayed side by side, and inputs an input image to the classifier associated with the performance information selected by the user.