Microscopy Image Generative Models for Privacy-Safe AI Training
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Solution Overview
Problem
Existing methods fail to efficiently merge trained models from different microscope users without disclosing sensitive information, and existing image sharing approaches either require sharing raw images or generate unsuitable training data.
Innovation Solution
Utilize generative models to generate synthetic image data sets that represent the original data, allowing training and analysis without sharing the actual images, thus protecting sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microscope images with sensitive data are shared to form a comprehensive model, then the model training quality is improved, but data security and privacy are compromised
Solution Approach 1:
The patent creates synthetic copies of microscope images through generative models instead of sharing real sensitive images. The generator network learns the distribution of real images and generates synthetic images that preserve statistical properties and training value while containing no actual sensitive information from patient samples
Solution Approach 2:
The patent introduces a generative model as an intermediary between the original sensitive data and the training process. This intermediary transforms real sensitive images into synthetic representations that mediate the training process, allowing model improvement without direct exposure to sensitive data
2Object-affected harmful factors
If sensitive image areas are pixelated or rendered noisy to protect privacy, then data security is improved, but the images become unsuitable for use as training data
Solution Approach 1:
Instead of degrading real images through pixelation or noise, the patent creates entirely new synthetic copies that preserve the essential statistical properties and structural characteristics needed for training, while containing no actual sensitive information from the original images
3Object-affected harmful factors
If a GAN model is trained to replace sensitive information with general content, then data security is improved, but the training data must closely resemble the input image which limits applicability to novel microscope images
Solution Approach 1:
The patent modifies the training objective and architectural parameters of the GAN to generate synthetic images based on statistical distributions rather than requiring close resemblance to specific input images. This allows the model to generalize to novel microscope images with different samples, carriers, or illumination conditions while maintaining security
4Object-affected harmful factors
If generated image content is created from statistical distribution already in training data, then data security is maintained, but the value as new training images is diminished
Solution Approach 1:
The patent generates synthetic copies that capture and preserve the statistical properties, structural patterns, and variability of the original training data distribution. These synthetic copies serve as valuable training images by providing additional samples that reflect the underlying data distribution without exposing actual sensitive images
Data Source
AI summary
In a method for processing microscope images, at least a first image data set (30) of a microscope (10) is received. At least a first generative model (40) that describes the first image data set (30) is estimated with a first computing device (20) based on the first image data set (30). Either a first generated image data set (50) is generated by the first generative model (40) and transmitted to a data exploitation device (60), or the first generative model (40) is transmitted to a data exploitation device (60) and subsequently a first generated image data set (50) is generated by means of the first generative model (40). Generated image data of the first generated image data set (50) is entirely data generated from the first generative model (40) and does not comprise processed image data of the first image data set (30) captured by the microscope (10). The first generated image data set (50) is then exploited by means of the data exploitation device (60).