Development of Computer Algorithms for Modeling Synthetic Images Based on Diffusion Models

Deep neural networks have enabled significant breakthroughs in medical image analysis. However, due to their high data requirement, small datasets in medical imaging tasks may prevent them from reaching their full potential. Synthetic data generation is a promising alternative to augment training da...

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Vydané v:Physics of particles and nuclei Ročník 56; číslo 6; s. 1628 - 1632
Hlavní autori: Shchetinin, E. Yu, Tiutiunnik, A. A.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Moscow Pleiades Publishing 01.12.2025
Springer Nature B.V
Predmet:
Algorithms
Artificial intelligence
Artificial neural networks
Computer vision
Data compression
Datasets
Deep learning
Diffusion models
Image analysis
Image resolution
Machine learning
Medical imaging
Medical research
Neural networks
Normal distribution
Particle and Nuclear Physics
Physics
Physics and Astronomy
Synthetic data
ISSN:1063-7796, 1531-8559, 1090-6495
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Shrnutí:Deep neural networks have enabled significant breakthroughs in medical image analysis. However, due to their high data requirement, small datasets in medical imaging tasks may prevent them from reaching their full potential. Synthetic data generation is a promising alternative to augment training datasets and enable medical image research on a larger scale. Recently, diffusion models have attracted the attention of the computer vision community because of their ability to generate photorealistic synthetic images. In this paper, we explore the possibilities of using diffusion models and develop computer algorithms for creating high-resolution synthetic images.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1063-7796
1531-8559
1090-6495
DOI:10.1134/S1063779625700996