Cognitive Assessment Image Sets With ML-Driven Diversity Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current cognitive and memory assessment methods rely on hand-curated image sets, which can compromise accuracy if reused, making repeated assessments difficult and prone to image leakage, thereby affecting the precision of tracking progression over time.
Innovation Solution
A machine learning-based approach generates diverse image sets using generative models to create test images that meet specific visual and semantic criteria, ensuring sufficient differences between images, allowing for longitudinal tracking and improved diagnostic precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hand-curated image sets are reused for repeated assessments, then assessment consistency is maintained, but image leakage occurs and assessment accuracy deteriorates
Solution Approach 1:
The system pre-generates multiple candidate image sets with controlled semantic and visual differences before assessments are needed. This preliminary generation ensures that when repeated assessments are required, fresh unique image sets are already available, preventing image leakage while maintaining assessment consistency and accuracy.
Solution Approach 2:
The system varies semantic parameters (object categories, attributes) and visual parameters (colors, positions, orientations) when generating image sets for different assessments. By changing these parameters, each assessment receives a unique image set that is semantically related but visually distinct, preventing image leakage while maintaining assessment reliability.
2Reliability
If unique image sets are generated for each assessment, then image leakage is prevented and assessment accuracy improves, but the complexity of image generation increases
Solution Approach 1:
The system introduces an intermediary image generation module that automatically creates unique image sets using controlled semantic and visual variations. This intermediary handles the complexity of generating diverse, unique images while maintaining consistency with assessment requirements, shielding the rest of the system from complexity while ensuring assessment accuracy.
Solution Approach 2:
The system generates copies of base image templates with modified semantic and visual parameters. Instead of creating entirely new images from scratch, it copies and adapts existing templates by varying attributes like color, position, and orientation, simplifying the generation process while ensuring uniqueness for each assessment.
3Measurement precision
If diverse image sets with sufficient semantic and visual differences are generated, then longitudinal tracking precision improves, but the time required for image generation increases
Solution Approach 1:
The system pre-generates multiple candidate image sets with controlled semantic and visual differences before assessments are needed. This preliminary generation ensures that when repeated assessments are required, fresh unique image sets are already available, preventing image leakage while maintaining assessment consistency and accuracy.
Solution Approach 2:
The system varies semantic parameters (object categories, attributes) and visual parameters (colors, positions, orientations) when generating image sets for different assessments. By changing these parameters, each assessment receives a unique image set that is semantically related but visually distinct, preventing image leakage while maintaining assessment reliability.
Data Source
AI summary
Techniques for generating image sets for cognitive assessment are provided. A first machine learning (ML) model may generate a plurality of candidate images that are each expected to meet a set of visual and semantic criteria. Each of the plurality of candidate images that meets the set of visual and semantic criteria may be identified as a test image. A second ML model may be used to generate a set of test images using the plurality of test images by adding to the set of test images, test images whose visual and semantic properties are sufficiently different from the visual and semantic properties of each other test image currently in the set of test images.