Post-Operative Anomaly Imaging With Multi-GAN SSIM Selection
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Solution Overview
Problem
Current diagnostic methods lack the ability to provide an accurate prognosis or post-operative status of anomalies, such as tumors, relying solely on practitioners' experience, which is insufficient for informed treatment decisions.
Innovation Solution
A method and system utilizing multiple generative adversarial networks (GANs) trained on pre-operative and post-operative medical images to generate and aggregate post-operative images of anomalies, employing Structural Similarity Index Measure (SSIM) for selecting and combining images to determine a final post-operative image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple GANs are used to generate post-operative images, then the accuracy and reliability of prognosis is improved, but the device complexity and computational resources required increase
Solution Approach 1:
Multiple GAN models are trained and integrated to generate post-operative images from pre-operative images. The system combines the outputs of several GANs through aggregation (pixel-wise averaging) to produce a final predicted post-operative image, thereby improving prognosis reliability through ensemble methodology
Solution Approach 2:
A selection module acts as an intermediary between the multiple GANs and the final output. This module evaluates the generated images using SSIM metrics and selects the best predictions before aggregation, serving as a mediator that optimizes the combination of multiple GAN outputs
2Measurement precision
If multiple GANs are trained and evaluated with SSIM scores, then the precision of treatment planning is improved, but the time required for image processing and analysis increases
Solution Approach 1:
Instead of evaluating all possible GAN outputs equally, the system uses SSIM metrics to identify and select only the top-performing predictions from each GAN. This partial selection approach maintains high precision while reducing the computational burden of processing all possible combinations
Solution Approach 2:
The GAN models are trained in advance on large datasets of pre-operative and post-operative images before actual prognosis generation. This preliminary training enables the models to make rapid predictions during clinical use, reducing real-time processing time while maintaining high precision
Data Source
AI summary
A method and a system for determining final post-operative images of an anomaly is disclosed. A processor inputs a pre-operative image of the anomaly to a first GAN, a second GAN, and a third GAN. Each of the first, second and third GANs are trained based on a training data that includes a training set of post-operative images of the anomaly corresponding to a training set pre-operative images of the anomaly. Further, a first post-operative image of the anomaly is determined from the first GAN, a second post-operative image of the anomaly is determined from the second GAN and a third post-operative image of the anomaly is determined from the third GAN. Two of the first, the second and the third post-operative images are selected based on a SSIM score of each of the first, the second and the third post-operative images.