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 and the retrospective nature of existing assessment criteria, leading to inadequate adaptation of treatment strategies and reduced patient survival rates.
Innovation Solution
The use of Generative Adversarial Networks (GANs) to generate synthetic training data for deep learning algorithms, enriching limited patient image data with realistic synthetic data, allowing for improved tumor lesion characterization and visualization, and the integration of Adaptive Sample Weighting (ASW) for enhanced training efficacy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If Generative Adversarial Networks (GANs) are used to generate synthetic training data, then the quantity of training data is increased, but the device complexity and computational resources required are increased
Solution Approach 1:
The patent applies the copying principle by using GANs to generate synthetic copies of medical imaging data. The generator network creates artificial images that mimic the statistical properties and visual characteristics of real patient data, enabling training on expanded datasets without requiring additional physical scans or patient information.
Solution Approach 2:
The patent implements preliminary action by pre-training the generator network on available real data before using it to produce synthetic training samples. This preparatory phase establishes the generator's ability to create realistic images, which are then used to augment the training dataset for the target medical imaging task.
2Loss of time
If deep learning algorithms are trained with limited patient data, then the training time is reduced, but the characterization accuracy and predictive performance deteriorate
Solution Approach 1:
The patent uses synthetic data copying to overcome the limitation of small training sets. By generating additional training examples through the GAN, the effective dataset size increases, allowing the deep learning model to learn more robust features and achieve better generalization performance without extending the actual training duration.
Solution Approach 2:
The patent applies parameter changes by modifying the data distribution parameters through synthetic generation. The GAN learns and reproduces the underlying data distribution characteristics, enabling the training algorithm to exposure to varied examples that reflect the true statistical properties of medical imaging data.
3Ease of operation
If traditional assessment criteria like RECIST are used, then the ease of operation is maintained, but the ability to predict future tumor growth and enable early treatment adaptation is reduced
Solution Approach 1:
The patent replaces the mechanical measurement system of RECIST (manual caliper measurements of lesion dimensions) with a deep learning-based image analysis system. This substitution enables the extraction of multiple quantitative features from images, including texture, shape, and intensity patterns, providing more comprehensive and predictive biomarkers for treatment response assessment.
Solution Approach 2:
The patent introduces deep learning algorithms as an intermediary between the raw medical images and the treatment decision-making process. This intermediary layer extracts and synthesizes multiple imaging features to generate predictive metrics that inform treatment adaptation, bridging the gap between static image measurements and dynamic treatment responses.
Data Source
AI summary
A method is for generating synthetic training data and for training deep learning algorithms for tumor lesion characterization. In an embodiment, the method for generating synthetic training data for training a deep learning algorithm includes training a Generative Adversarial Network to generate synthetic image data, the Generative Adversarial Network including a generator and a discriminator; and using the generator of the Generative Adversarial Network to generate synthetic image data as the synthetic training data.