Domain Matching Converter for Transportable Imaging Analytics
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Solution Overview
Problem
Deep learning models for imaging applications face challenges in transportability due to performance drops when applied to different datasets from varying experimental and imaging conditions, requiring domain matching for effective deployment across diverse applications, and existing methods like transfer learning need target domain annotations which may not be available.
Innovation Solution
A method for domain matching image conversion that converts images from the target domain to mimic the source domain, allowing imaging application analytics to be directly applied, without requiring target domain training data, using encoders and decoders to create a domain converter that can be trained with a single unannotated image from the target domain, enabling efficient conversion for specific imaging applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If transfer learning with fine-tuning is used to improve model transportability, then annotation cost is reduced by about 20%, but target domain annotations are still required which may not be available
Solution Approach 1:
Instead of adapting the model to the target domain through fine-tuning with target domain annotations, the patent inverts the approach by converting target domain images to match the source domain. This allows the pre-trained model to be applied directly without requiring target domain annotations, resolving the contradiction by eliminating the annotation requirement while maintaining transportability.
Solution Approach 2:
The patent introduces an image conversion system as an intermediary between the target domain images and the pre-trained model. This converter transforms target domain images into source domain style, enabling the model to process them without direct exposure to target domain data, thus avoiding the need for target domain annotations while achieving effective transportability.
2Reliability
If domain adaptation methods are applied to improve model performance across domains, then some target domain annotations are needed, but these annotations may not be practical to obtain
Solution Approach 1:
The patent inverts the traditional domain adaptation paradigm by not adapting the model to the target domain, but rather transforming the target domain images to match the source domain. This inversion eliminates the need for target domain annotations while preserving model performance, as the pre-trained model processes converted images that match its training distribution.
3Device complexity
If image normalization and calibration are used to match target domain images to source domain images, then the approach is simple, but application critical features are not preserved
Solution Approach 1:
The patent introduces a sophisticated image conversion system as an intermediary that goes beyond simple normalization and calibration. This converter uses deep learning techniques to transform target domain images into source domain style while preserving application critical features, achieving both reasonable complexity and high feature preservation accuracy.
Data Source
AI summary
A computerized domain matching image conversion method for transportable imaging applications first performs a target domain A to source domain B matching converter training by computing means using domain B training images and at least one domain A image to generate an A to B domain matching converter. The method then applies the A to B domain matching converter to a domain A application image to generate its domain B matched application image. The method further applies a domain B imaging application analytics to the domain B matched application image to generate an imaging application output for the domain A application image.