Cell Image Discriminator Evaluation for Reliable ROI Analysis
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Solution Overview
Problem
Users cannot independently verify the appropriateness of discriminators used for analyzing cell images constructed by machine learning, as these models are typically pre-provided and their suitability is unknown.
Innovation Solution
A cell image analysis device with a display unit, storage unit, test data input, discriminator evaluation unit, and evaluation result storing unit allows users to evaluate discriminators using test data and display results, enabling self-assessment of discriminator appropriateness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a discriminator created by machine learning is used to analyze phase images, then the analysis can be performed regardless of user skill, but the user cannot independently verify the appropriateness of the discriminator
Solution Approach 1:
The system introduces feedback by allowing users to input test data and automatically displaying the evaluation results of the discriminator. This feedback loop enables users to verify the appropriateness of the discriminator for their specific images without needing to understand the underlying machine learning algorithms, thus maintaining ease of operation while improving reliability through independent verification.
Solution Approach 2:
The system enables self-service by automatically evaluating the discriminator using user-provided test data and displaying the results. Users can independently assess whether the discriminator is suitable for their images without requiring external expertise or manual verification, empowering them to make informed decisions about the analysis results.
2Extent of automation
If a learning model is constructed by machine learning using learning data, then automated analysis is achieved, but the suitability of the discriminator for actual images remains unknown
Solution Approach 1:
The system performs preliminary action by automatically evaluating the discriminator with test data before the user relies on the analysis results. This preliminary verification step ensures that the discriminator is appropriate for the specific images to be analyzed, preventing loss of information due to inappropriate model application while maintaining the automated analysis capability.
Solution Approach 2:
The system uses feedback by automatically comparing the discriminator's performance on test data against expected outcomes and displaying the evaluation results. This feedback mechanism informs users about the suitability of the automated analysis for their specific case, preventing information loss while preserving the benefits of automation.
3Adaptability or versatility
If various algorithms for removing noise or background are provided, then image processing flexibility is improved, but skill in image analysis is required to select the appropriate algorithm
Solution Approach 1:
The system applies self-service by automatically selecting and applying the appropriate noise removal or background subtraction algorithm based on the image characteristics. The discriminator evaluation process automatically determines the most suitable algorithm without requiring user intervention or expertise, thereby maintaining adaptability while improving ease of operation.
Solution Approach 2:
The system uses an intermediary mechanism where the discriminator evaluation unit acts as a mediator between the image input and the appropriate algorithm selection. This intermediary automatically assesses the image characteristics and selects the most suitable processing algorithm, eliminating the need for users to have specialized knowledge while preserving adaptability to different image types.
Data Source
AI summary
A cell image analysis device includes: a display unit; a storage unit which stores learning data which is a set of data of a cell image and data of an analyzed image in which a region of interest included in the cell image is specified, and a discriminator which is created using a learning model constructed by machine learning using the learning data; a test data input receiver to receive an input of test data; a discriminator evaluation unit to evaluate the discriminator using the test data; an evaluation result storing unit to store evaluation data including the evaluation result of the discriminator and the test data used for the evaluation in association with the discriminator; and a display processor to display the test data and/or the evaluation result associated with the discriminator on a screen of the display unit in response to an input for selecting the discriminator.