GAN Synthetic Data for Tumor Lesion Characterization
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Solution Overview
Problem
Current methods for tumor lesion characterization, particularly in lung nodules and colorectal cancer, face challenges due to limited data availability, relying heavily on radiologist experience and manual feature extraction, which are inefficient and do not allow for real-time adaptation of treatment based on tumor growth predictions.
Innovation Solution
The use of Generative Adversarial Networks (GANs) to generate synthetic training data for deep learning algorithms, combined with Adaptive Sample Weighting (ASW) and sparse representations, enables effective tumor lesion characterization with limited data, allowing for improved classification performance and early assessment of tumor growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning algorithms are used for tumor lesion characterization, then classification performance is improved, but data availability becomes insufficient
Solution Approach 1:
The patent uses Generative Adversarial Networks (GANs) to generate synthetic copies of medical images that mimic real patient data. The generator network creates artificial images while the discriminator network evaluates their authenticity, producing synthetic training data that expands the available dataset without requiring additional real patient scans.
Solution Approach 2:
The patent applies Adaptive Sample Weighting (ASW) to dynamically adjust the importance weights of different training samples during the learning process. This allows the model to adaptively focus on more informative or difficult samples, optimizing training efficiency and performance with limited data by changing the parameter weights rather than requiring more data.
2Quantity of substance
If manual feature extraction by radiologists is used, then data requirements are reduced, but classification efficiency and accuracy deteriorate
Solution Approach 1:
The patent replaces the manual mechanical process of radiologist feature extraction with an automated deep learning system. Instead of radiologists manually analyzing images and extracting features, the system uses trained neural networks to automatically perform feature extraction and classification, significantly improving efficiency while maintaining or enhancing accuracy.
Solution Approach 2:
The deep learning system performs self-service by automatically extracting features and making classifications without requiring manual radiologist intervention for each case. The model learns from training data and then independently characterizes new tumor lesions, reducing the time and labor required while improving consistency.
3Measurement precision
If extensive training data is collected, then model performance is improved, but training time and computational resources increase
Solution Approach 1:
The patent uses Adaptive Sample Weighting to dynamically adjust training parameters during the learning process. By changing the weights assigned to different samples based on their informational value and difficulty, the system optimizes the training process to achieve better performance with fewer iterations and less computational time than traditional uniform weighting approaches.
Solution Approach 2:
The patent generates only the necessary amount of synthetic data through GANs rather than collecting or generating exhaustive datasets. The ASW technique also focuses computational effort on the most informative samples, applying partial action principles to achieve sufficient model performance without the excessive time and resources required for comprehensive data collection and uniform training approaches.
Data Source
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AI summary
Method for generating synthetic training data for training a deep learning algorithm (1), comprising the steps of: - training a Generative Adversarial Network (5) to generate synthetic image data (4), wherein the Generative Adversarial Network (5) comprises a generator (6) and a discriminator (7), - using the generator (6) of the Generative Adversarial Network (5) to generate synthetic image data (4) as the synthetic training data.