Synthetic Medical Image Generation With Cycle-Consistency Loss
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Solution Overview
Problem
Existing machine learning models for cross-modality image translation in medical imaging are difficult to train, require extensive hyperparameter tuning, and often suffer from overfitting, leading to unrealistic or mismatched synthetic images.
Innovation Solution
A method involving a conditional generative model is trained using an image encoder and a conditional generative model to generate synthetic medical images by reducing deviations through model parameter modification, utilizing image embeddings and data sets from different imaging techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If Convolutional Neural Networks are used for cross-modality image translation, then image synthesis capability is achieved, but training difficulty increases and overfitting occurs
Solution Approach 1:
The patent segments the training process into multiple stages with different loss functions. First, a cycle-consistency loss is applied to ensure structural accuracy. Second, a perceptual loss is applied to preserve visual quality. This segmentation allows the model to focus on different aspects of image synthesis separately, reducing overall training complexity while improving reliability.
Solution Approach 2:
The patent implements feedback mechanisms through cycle-consistency constraints and perceptual loss functions that continuously monitor and adjust the generation process. The model compares synthesized images against original images and adjusts parameters accordingly, providing feedback loops that prevent overfitting and improve synthesis quality without requiring excessive training complexity.
2Reliability
If extensive hyperparameter tuning is performed, then model performance may improve, but training time and computational resources increase
Solution Approach 1:
The patent changes the approach to parameter optimization by using multiple loss functions with different weights rather than extensive hyperparameter tuning. The cycle-consistency loss weight and perceptual loss weight are adjusted to balance structural accuracy and visual quality, respectively. This parameter change strategy achieves reliable model performance without requiring extensive time-consuming hyperparameter optimization.
3Adaptability or versatility
If existing machine learning models are used for image synthesis, then cross-modality translation is achieved, but the generated images become unrealistic or do not match training data
Solution Approach 1:
The patent segments the image synthesis process into structural reconstruction (using cycle-consistency loss) and visual refinement (using perceptual loss). This segmentation ensures that the model first achieves accurate cross-modality translation and then refines the output to be realistic and visually consistent with training data, resolving the contradiction between adaptability and precision.
Solution Approach 2:
The perceptual loss function provides feedback about visual quality and realism, continuously adjusting the synthesis process to ensure generated images match the aesthetic and structural characteristics of training data. This feedback mechanism maintains both cross-modality translation capability and image realism simultaneously.
Data Source
AI summary
Systems, methods, and computer programs disclosed herein relate to training a machine learning model and using the trained machine learning model to generate synthetic medical images.