Diagnostic Tube Assembly Imaging With Synthetic Variations
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Solution Overview
Problem
Existing diagnostic laboratory systems face challenges in efficiently adapting to new tube assembly configurations due to the high cost and time required for retraining machine learning models, as they struggle to handle the vast array of tube assembly variations from different manufacturers and third-party sources.
Innovation Solution
The system employs image-to-image translation using generative adversarial networks (GANs) to synthesize images of tube assemblies, decomposing and manipulating features in latent space to generate controlled variations, enabling efficient adaptation of machine learning models to recognize new tube assembly configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are retrained to characterize new tube assembly configurations, then the system can handle new tube assembly variations, but the training cost and time become excessive
Solution Approach 1:
The patent generates synthetic images of tube assemblies by copying and manipulating features from existing training images. Instead of retraining the model on new physical tube assemblies, the system creates artificial representations that preserve the essential visual characteristics, allowing the model to generalize to new configurations without additional retraining
Solution Approach 2:
The system manipulates parameters such as cap colors, tube colors, and assembly configurations in the latent space of the trained model. By changing these parameters synthetically, the system generates diverse tube assembly variations that expand the model's adaptability without requiring retraining
2Adaptability or versatility
If machine learning models are retrained to characterize new tube assembly configurations, then the system can handle new tube assembly variations, but the training cost becomes excessive
Solution Approach 1:
The patent creates synthetic training data by copying and transforming existing images through latent space manipulation. This eliminates the need for expensive data collection, annotation, and model retraining processes, significantly reducing the computational cost while maintaining adaptability to new tube assembly types
3Reliability
If extensive training data is collected to cover all tube assembly configurations, then the machine learning model can be trained comprehensively, but it becomes difficult to collect every possible variation
Solution Approach 1:
The system uses its own trained model to generate synthetic training data for new tube assembly configurations. Instead of relying on external data collection efforts, the model serves itself by creating the training data it needs, automatically covering configurations that would be difficult or impossible to collect manually
Solution Approach 2:
The patent performs preliminary feature extraction and latent space learning from a limited set of training images before generating synthetic variations. This preliminary action creates a comprehensive representation of tube assembly features that can be combined in countless ways, achieving broad coverage without extensive data collection
Data Source
AI summary
A method of synthesizing an image of a tube assembly includes capturing an image of the tube assembly, wherein the capturing generates a captured image. The captured image is decomposed into a plurality of features in latent space using a trained image decomposition model. One or more of the features in the latent space is manipulated into one or more manipulated features. A synthesized tube assembly image is generated with at least one of the manipulated features using a trained image composition model. Other methods and systems are disclosed.