Unsupervised Domain Adaptation for CT Lung Texture Recognition
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Solution Overview
Problem
Deep network models trained on one type of CT image data struggle to generalize effectively to different types of CT image data due to variations in noise and imaging, leading to reduced recognition accuracy when applied to new data without manual labeling of typical lung textures.
Innovation Solution
An unsupervised content-preserved domain adaptation method using an adversarial learning mechanism and a content consistency network module fine-tunes a pre-trained deep network model to maintain high performance in lung texture recognition across different CT image data types without requiring manual labeling of target domain data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a deep network model is trained on one type of CT image data, then recognition accuracy on the same type of data is improved, but generalization to different types of CT image data deteriorates
Solution Approach 1:
The patent applies parameter changes by modifying the network model's parameters through adversarial domain adaptation. The domain adapter learns to transform source domain features to match target domain distributions by adjusting network parameters during fine-tuning, enabling the model to adapt to different CT image types while maintaining recognition accuracy
Solution Approach 2:
The patent introduces a domain adapter as an intermediary component between the pre-trained network and the target domain data. This adapter acts as a mediator that aligns feature distributions across domains through adversarial training, allowing the model to generalize to different CT image types without retraining the entire network
2Adaptability or versatility
If different types of CT image data are collected and manually labeled to train the network model, then generalization to different types of CT image data is improved, but time consumption and labor cost increase
Solution Approach 1:
The patent implements self-service through unsupervised domain adaptation where the model automatically adapts to target domain data without requiring manual labeling. The adversarial training mechanism enables the system to self-adjust and align domain distributions autonomously, eliminating the need for time-consuming manual annotation of target domain images
Solution Approach 2:
The patent applies preliminary action by pre-training the network on source domain data before deployment. This pre-trained model serves as a foundation that can be quickly adapted to target domains through the domain adapter, avoiding the need to collect and label large amounts of target domain data from scratch
Data Source
AI summary
The invention discloses an unsupervised content-preserved domain adaptation method for multiple CT lung texture recognition, which belongs to the field of image processing and computer vision. This method enables the deep network model of lung texture recognition trained in advance on one type of CT data (on the source domain), when applied to another CT image (on the target domain), under the premise of only obtaining target domain CT image and not requiring manually label the typical lung texture, the adversarial learning mechanism and the specially designed content consistency network module can be used to fine-tune the deep network model to maintain high performance in lung texture recognition on the target domain. This method not only saves development labor and time costs, but also is easy to implement and has high practicability.