Parameter Transformation for Medical Image Model Training
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Solution Overview
Problem
Deep learning models for medical imaging require extensive data and computational resources, leading to high training costs and limited applicability due to the complexity and distribution of medical data, which hinders the development of effective models for diagnosis and treatment.
Innovation Solution
The method involves transferring parameters from a pre-trained model in a different application scenario to a new model, allowing it to start optimization from an ideal point, reducing training time and costs, and enhancing model accuracy through parameter transformation methods such as complete, partial, hybrid, timing, and customized import methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deep learning model training is performed from scratch on medical imaging data, then the model can achieve accurate diagnosis and treatment analysis, but the training time and computational resources required become excessively long and costly
Solution Approach 1:
The patent applies preliminary action by pre-training a deep learning model on general medical image data before fine-tuning it on specific disease types. The pre-trained model learns general features and patterns from diverse medical images, which are then transferred to the target disease analysis task, significantly reducing the training time required while maintaining high accuracy.
2Measurement precision
If large-scale deep learning models are trained to handle complex medical image patterns, then the model recognition capability improves, but the computational resources and training costs increase exponentially
Solution Approach 1:
The patent changes parameters by adjusting the model architecture and training parameters based on the specific disease type and available data. Instead of always using large-scale models, the system adapts model complexity to match the task requirements, reducing computational resource consumption while maintaining sufficient pattern recognition capability for medical diagnosis.
Solution Approach 2:
The pre-training phase allows the model to learn general medical image features from diverse data sources before being fine-tuned on specific disease patterns. This preliminary learning reduces the computational burden during the fine-tuning stage, as the model already possesses foundational knowledge that requires less computational resources to adapt.
3Measurement precision
If deep learning models are trained on disease-specific data only, then the model achieves high accuracy for that specific disease, but the training data volume is insufficient due to data distribution across different hospitals
Solution Approach 1:
The patent applies universality by training the model on multiple disease types and general medical image data, creating a multi-functional model that can adapt to various disease analysis tasks. The pre-trained model learns universal features from diverse medical images across different hospitals and disease types, making the training data more sufficient and reducing the impact of data distribution limitations.
4Adaptability or versatility
If the model architecture is increased in complexity to handle diverse medical image types and disease patterns, then the model versatility improves, but the device complexity and training difficulty increase
Solution Approach 1:
The patent applies dynamics by making the model architecture adaptable and configurable based on the specific application requirements. The system can dynamically adjust the model complexity and architecture depending on the disease type, available data, and computational resources, achieving high versatility without permanently increasing device complexity.
Data Source
AI summary
A method and device for performing based learning on a medical image includes reading raw data of a medical image, performing transformation processing on the data by analyzing a data attribute, and integrating the same into a data format capable of being received by a model to be trained; selecting a transformation method by comparing parameters of the model to be trained and a trained model, so as to perform parameter transformation and apply transformation-based learning to training of the model to be trained for the medical image; and upon finishing model training, applying a parameter of a trained model to image category analysis. The invention further includes a device for performing transformation-based learning on a medical image, including: a data processing module; a transformation-based learning module; and an application module.