GAN with BERT Discriminator for Medical Image Translation
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Solution Overview
Problem
Current approaches using neural networks trained with generative adversarial networks are limited in their ability to effectively generate or translate medical images, such as from magnetic resonance imaging (MRI) to positron emission tomography (PET) images, due to constraints in the range and type of medical data they can handle.
Innovation Solution
The use of a generative adversarial network (GAN) with a bidirectional encoder representations from transformers (BERT) discriminator to train neural networks for translating MRI images into PET images, allowing for the generation of high-quality medical images by leveraging advanced data processing and representation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current neural network approaches with GAN are used for medical image translation, then image generation capability is provided, but the range and type of medical data that can be handled is limited
Solution Approach 1:
The patent applies universality by training a single neural network model to handle multiple types of medical image translation tasks (e.g., MRI to PET, CT to MRI) simultaneously. The GAN architecture is designed with a universal discriminator that can evaluate different image types and a generator capable of learning multiple translation mappings, thereby expanding the range of medical data the system can process while maintaining reliable image synthesis quality through unified training on diverse datasets
2Manufacturing precision
If advanced data processing techniques like BERT discriminator are employed, then image translation quality is improved, but computational complexity and resource requirements increase
Solution Approach 1:
The patent introduces BERT as an intermediary component in the GAN architecture, specifically as a discriminator that leverages pre-trained language model capabilities to better evaluate the realism and accuracy of translated medical images. This intermediary BERT discriminator acts as a sophisticated evaluator that improves image translation quality by providing more nuanced feedback to the generator, while the pre-trained nature of BERT helps manage computational complexity by avoiding training from scratch
3Measurement precision
If GAN with BERT discriminator is used for training, then high-quality PET image generation from MRI is achieved, but training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by utilizing pre-trained BERT models for the discriminator component before fine-tuning on medical image data. This pre-training provides the discriminator with robust general language understanding and feature extraction capabilities, which are then adapted to medical image evaluation. This preliminary preparation reduces the training time required to achieve high-quality PET image generation from MRI, as the model starts from a stronger baseline rather than training all components from scratch
Data Source
AI summary
Apparatuses, systems, and techniques to facilitate generation of one medical image from another medical image using one or more neural networks trained using a generative adversarial network (GAN) that utilizes a bidirectional encoder representations from transformers (BERT) as a discriminator. In at least one embodiment, one or more neural networks trained using a GAN comprising a BERT discriminator generate a positron emission tomography (PET) image from a magnetic resonance imaging (MRI) image.