Post-Processing Model Correction for Medical Image Domain Adaptation
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Solution Overview
Problem
AI-based medical image processing models, such as organ segmentation models, perform poorly when applied to images from different domains due to variations in protocol, demography, or imaging modalities, leading to inaccurate predictions.
Innovation Solution
A system that includes a post-processing model, specifically a shape autoencoder, is trained to correct the inference outputs of source image processing models when applied to target domain images, using unsupervised or semi-supervised machine learning methods, allowing for domain adaptation without requiring extensive labeled data from the target domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a source image processing model is trained on source domain images and applied to target domain images, then the model can be used without retraining, but the prediction accuracy deteriorates due to domain variations
Solution Approach 1:
A post-processing model is introduced as an intermediary between the source domain model and the final output. This post-processing model is trained on target domain data to correct the predictions of the source domain model, thereby maintaining both ease of deployment and high accuracy on target domain images
Solution Approach 2:
The system changes the parameters of the prediction output by applying a correction model that learns the domain-specific adjustments needed. The post-processing model modifies the source model's predictions by learning domain-specific parameter transformations from target domain training data
2Measurement precision
If the source image processing model is retrained on target domain data, then the prediction accuracy on target domain images improves, but the complexity of the system increases
Solution Approach 1:
The system segments the model adaptation task into two independent components: the source domain model for general feature extraction and the post-processing model for domain-specific correction. This segmentation allows each component to be optimized independently, reducing overall system complexity while maintaining high accuracy
3Measurement precision
If extensive labeled target domain data is collected for model retraining, then the prediction accuracy improves, but the time and resources required for data collection and model retraining increase
Solution Approach 1:
The post-processing model is trained in advance on available target domain data, performing preliminary adaptation before actual deployment. This preliminary action allows the model to be ready for high-accuracy predictions without requiring real-time data collection or retraining when new target domain images need to be processed
Data Source
AI summary
Techniques are described for domain adaptation of image processing models using post-processing model correction According to an embodiment, a method comprises training, by a system operatively coupled to a processor, a post-processing model to correct an image-based inference output of a source image processing model that results from application of the source image processing model to a target image from a target domain that differs from a source domain, wherein the source image processing model was trained on source images from the source domain. In one or more implementations, the source imaging processing model comprises an organ segmentation model and the post-processing model can comprise a shape-autoencoder. The method further comprises applying, by the system, the source image processing model and the post-processing model to target images from the target domain to generate optimized image-based inference outputs for the target images.