Pre-trained Learning Network for Medical Image Analysis
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Solution Overview
Problem
Existing deep learning model training methods for medical image analysis face challenges due to insufficient data, noise, and artifacts, leading to high complexity and low efficiency in clinical applications.
Innovation Solution
A training method that involves pre-training a learning network using an original data set, evaluating for defects, performing data augmentation or acquiring new data, and refining the training using a data augmented set to improve the accuracy and efficiency of medical image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning is used for medical image analysis, then automatic feature extraction and diagnosis accuracy are improved, but data collection time and training complexity increase
Solution Approach 1:
The patent applies preliminary action by using pre-trained models on large-scale general image datasets before fine-tuning on medical images. This preliminary training on abundant general data eliminates the need to collect and train on large amounts of medical data from scratch, significantly reducing data collection time while maintaining diagnostic accuracy through subsequent specialized fine-tuning.
2Measurement precision
If more medical image data is collected for training, then model accuracy is improved, but data acquisition difficulty and training complexity increase
Solution Approach 1:
The patent performs preliminary training on large-scale general image datasets before fine-tuning on smaller medical datasets. This two-stage approach allows the model to learn general image features from abundant data, then adapt to medical specifics with limited data, achieving high accuracy without requiring extensive medical data collection or complex training procedures.
Solution Approach 2:
The patent employs parameter changes by adjusting learning rates, training epochs, and optimization parameters during the fine-tuning stage. By carefully controlling these training parameters when transitioning from general to medical images, the model achieves high accuracy on medical data without requiring proportionally large increases in data volume or training complexity.
3Productivity
If deep learning models are trained on limited medical data, then training speed is improved, but model accuracy and generalization capability deteriorate
Solution Approach 1:
The patent uses preliminary training on large-scale general datasets to establish strong foundational features before fine-tuning on limited medical data. This preliminary action ensures the model already possesses robust image understanding capabilities, allowing it to achieve high medical image accuracy quickly during fine-tuning without requiring extensive medical data or prolonged training.
Solution Approach 2:
The patent leverages knowledge copied from general image recognition tasks by transferring pre-trained model weights to medical image analysis. This knowledge transfer allows the model to retain general image processing capabilities while adapting to medical specifics with limited data, maintaining both training speed and accuracy through efficient knowledge reuse rather than learning from scratch.
4Reliability
If data augmentation is performed to increase training data, then model robustness is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by performing data augmentation selectively during the fine-tuning stage on medical images, rather than throughout the entire training process. This targeted approach enhances model robustness for medical-specific variations while avoiding the excessive processing time that would result from augmenting the entire training pipeline, maintaining efficiency while improving reliability.
Data Source
AI summary
The present disclosure relates to a training method and a training system for training a learning network for medical image analysis. The training method includes: acquiring an original training data set for a learning network with a predetermined structure; performing, by a processor, a pre-training on the learning network using the original training data set to obtain a pre-trained learning network; evaluating, by the processor, the pre-trained learning network to determine whether the pre-trained learning network has an evaluation defect; when the pre-trained learning network has the evaluation defect, performing, by the processor, a data augmentation on the original training data set for the existing evaluation defect; and performing, by the processor, a refined training on the pre-trained learning network using a data augmented training data set. The present disclosure can evaluate and train the learning network in stages, therefore, the complexity of medical image processing is reduced, and the efficiency and accuracy of medical image analysis are improved.