Synthetic Biological Image Pipeline With Parametric Feature Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI training methods for image recognition, particularly in medical and biological samples, require large amounts of manually annotated real images, which are costly, time-consuming, and prone to errors, and existing synthetic image generation techniques like GANs lack control over the generated results and do not provide necessary sample solutions.
Innovation Solution
A method involving a pipeline of parametric algorithms that generate synthetic images of biological materials, allowing controlled variation of features to produce photorealistic images with associated sample solutions, eliminating the need for extensive real image collection and manual annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manually annotated real images are used for AI training, then training data represents real-world conditions, but the process is costly, time-consuming, and error-prone
Solution Approach 1:
The patent generates synthetic images that copy and replicate the essential visual characteristics of real biological samples while eliminating the need for manual annotation. The synthetic images are created using procedural generation techniques that produce photorealistic appearances matching real tissue sections, cell cultures, and other biological materials, thereby providing training data without time-consuming human annotation.
Solution Approach 2:
The patent employs parametric algorithms that systematically vary multiple parameters (staining intensity, tissue density, cellular structure, illumination conditions) to generate diverse synthetic images. This parameter-based approach enables controlled variation of image characteristics while maintaining photorealism, allowing comprehensive training data generation without manual intervention.
2Productivity
If existing synthetic image generation techniques like GANs are used, then training data can be generated without manual annotation, but the generated images lack control over specific features and do not provide necessary sample solutions
Solution Approach 1:
The patent implements a dynamic procedural generation pipeline where parametric algorithms can be adjusted in real-time to control specific image features. The system allows dynamic variation of parameters such as staining characteristics, tissue architecture, cellular morphology, and illumination conditions, enabling precise control over the generated images while maintaining high productivity.
Solution Approach 2:
The patent incorporates feedback mechanisms where the generated synthetic images are evaluated and refined based on required training objectives. The parametric algorithms receive feedback about desired image characteristics and automatically adjust parameters to achieve the needed precision in feature representation, ensuring both productivity and manufacturing precision.
3Adaptability or versatility
If large amounts of diverse real images are collected for training, then AI can handle variability in biological samples, but the collection process is resource-intensive and time-consuming
Solution Approach 1:
The patent uses parametric algorithms that systematically vary multiple parameters to generate a large quantity of diverse synthetic images. By controlling parameters such as tissue type, staining intensity, cellular density, illumination conditions, and sample preparation variations, the system generates extensive training data that covers the full range of biological variability without requiring physical collection of numerous real samples.
Solution Approach 2:
The patent creates a universal synthetic image generation system that can produce multiple types of biological images (tissue sections, cell cultures, smears) by adjusting a set of core parameters. This multi-functional approach allows the same procedural generation framework to create diverse training data for different biological samples, reducing the need for separate data collection processes for each sample type.
Data Source
AI summary
A method for generating synthetic images of a biological material and a sample solution for training an artificial intelligence (AI) by: generating a base data set comprising image and coordinate data, feeding the base data set to a pipeline of conversion nodes, each comprising a parametric algorithm to generate a subsequent image dataset by changing or adding a feature of the image dataset, each parametric algorithm comprising a parameter to be varied within a range of values to control the change of the feature during the conversion, wherein, each subsequent image data set in the pipeline is sequentially fed to a plurality of conversion nodes to generate a final image data set representing the synthetic image, and generating the sample solution with a further algorithm for the final image data set or a subsequent image data set preceding the final image data set in the pipeline.