Synthetic Medical Image Filtering for Privacy-Safe Model Fine-Tuning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Continual learning of deep learning neural networks for medical imaging is hindered by privacy restrictions that prevent the sharing of real-world medical images, necessitating the development of systems that can maintain network performance without violating privacy laws.
Innovation Solution
A system that synthesizes and filters synthetic variants of privacy-restricted medical images to re-train or fine-tune the neural network, using a second deep learning neural network to generate images resembling the original while adhering to privacy regulations, and a filtration process to ensure the synthetic images are relevant for training without violating privacy laws.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-world medical images are used for continual learning, then the deep learning neural network's performance can be maintained over time, but privacy restrictions prevent access to these images
Solution Approach 1:
The patent creates synthetic copies of medical images using a generative adversarial network (GAN). The GAN generates realistic-looking medical images that replicate the statistical properties and visual characteristics of real medical images without containing actual patient data. These synthetic images serve as substitutes for real images in continual learning, maintaining network performance while eliminating privacy concerns.
Solution Approach 2:
The patent introduces a synthetic image generation system as an intermediary between the need for real medical images and privacy protection requirements. This intermediary system produces intermediate representations (synthetic images) that satisfy both the learning needs and privacy constraints, acting as a mediator that resolves the contradiction between data availability and privacy protection.
2Object-affected harmful factors
If synthetic images are generated to replace real medical images, then privacy compliance is achieved, but the quality and realism of training images may be compromised
Solution Approach 1:
The patent employs parameter changes in the GAN architecture and training process to optimize the quality of synthetic images. By adjusting hyperparameters, network architecture parameters, and training parameters (such as loss function weights, learning rates, and batch sizes), the system achieves a balance between privacy protection and image quality. The parameter optimization ensures that synthetic images maintain sufficient realism and statistical properties for effective continual learning.
Data Source
AI summary
Systems or techniques that facilitate medical image privacy preservation via image synthesis and filtration are provided. In various embodiments, a system can access a medical image for which a first artificial neural network has produced an inferencing task result. In various aspects, the system can train a second artificial neural network on the medical image to perform image synthesis. In various instances, the system can generate, via execution of the second artificial neural network post-training, a set of synthetic variants of the medical image. In various cases, the system can fine-tune the first artificial neural network using at least some of the set of synthetic variants of the medical image rather than using the medical image.