CNNs trained only on diffusion-generated synthetic medical images achieved 72-91% accuracy on real test images across three domains, but no comparison to models trained on real data was performed.
A Novel Framework for Selection of GANs for an Application
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Generative Adversarial Network (GAN) is a current focal point of research. The body of knowledge is fragmented, leading to a trial-error method while selecting an appropriate GAN for a given scenario. We provide a comprehensive summary of the evolution of GANs starting from its inception addressing issues like mode collapse, vanishing gradient, unstable training and non-convergence. We also provide a comparison of various GANs from the application point of view, its behaviour and implementation details. We propose a novel framework to identify candidate GANs for a specific use case based on architecture, loss, regularization and divergence. We also discuss application of the framework using an example, and we demonstrate a significant reduction in search space. This efficient way to determine potential GANs lowers unit economics of AI development for organizations.
citation-role summary
background 1
citation-polarity summary
fields
eess.IV 1years
2024 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Diffusion-Based Approaches in Medical Image Generation and Analysis
CNNs trained only on diffusion-generated synthetic medical images achieved 72-91% accuracy on real test images across three domains, but no comparison to models trained on real data was performed.