Tissue Nodule Detection via Domain Adaptation Loss
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Solution Overview
Problem
Conventional tissue nodule detection methods face challenges in accuracy due to variations in medical imaging devices and datasets, leading to differences in image distributions between training and actual detection efforts.
Innovation Solution
A method involving neural network models to extract features from source and target domain data, determining a distance parameter based on maximum mean discrepancy (MMD), and iteratively reducing a combination of loss function values and distance parameters to train a tissue nodule detection model, improving feature consistency across domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional tissue nodule detection methods are used, then the detection process is simple, but the detection accuracy is poor due to distribution differences between training and actual data
Solution Approach 1:
The patent changes the training objective from standard classification loss to domain adaptation loss by incorporating Maximum Mean Discrepancy (MMD) as a regularization term. This parameter change in the loss function transforms the model to learn domain-invariant features, improving detection accuracy across different imaging devices while managing complexity through gradient-based optimization
Solution Approach 2:
The patent introduces domain adaptation as an intermediary mechanism between source domain training data and target domain actual images. By using MMD to measure and minimize distribution distance, it creates a bridge that allows the model to generalize better from training to deployment across different medical imaging devices
2Adaptability or versatility
If standard neural network training is used, then the training process is straightforward, but the model fails to adapt to different imaging devices and datasets
Solution Approach 1:
The patent modifies the training objective function by adding MMD regularization term with hyperparameter λ to balance source domain accuracy and target domain adaptability. This parameter change enables the model to simultaneously learn from annotated source data while adapting to unannotated target domain characteristics, improving cross-device versatility without requiring extensive retraining
Solution Approach 2:
The patent performs preliminary domain adaptation training using source domain data with annotations before deployment on target domain data. By pre-learning domain-invariant features through MMD regularization during the training phase, the model is prepared to handle distribution shifts when deployed across different imaging devices, reducing the need for time-consuming post-deployment adjustments
Data Source
AI summary
This application relates to a tissue nodule detection and tissue nodule detection model training method, apparatus, device, storage medium and system. The method for training a tissue nodule detection model includes: obtaining source domain data and target domain data, the source domain data comprising a source domain image and an image annotation, the target domain data comprising a target image, and the image annotation being used for indicating location information of a tissue nodule in the source domain image; performing feature extraction on the source domain image using a neural network model to obtain a source domain sampling feature, performing feature extraction on the target image using the neural network model to obtain a target sampling feature, and determining a model result according to the source domain sampling feature using the neural network model; determining a distance parameter between the source domain data and the target domain data according to the source domain sampling feature and the target sampling feature, the distance parameter being a parameter describing a magnitude of a data difference between the source domain data and the target domain data; determining, according to the model result and the image annotation, a loss function value corresponding to the source domain image; and training the neural network model to obtain a tissue nodule detection model by iteratively reducing a combination of the loss function value and the distance parameter. In this way, the detection accuracy can be improved.