Survey on sampling conditioned brain images and imaging measures with generative models

Generative models have become innovative tools across various domains, including neuroscience, where they enable the synthesis of realistic brain imaging data that captures complex anatomical and functional patterns. These models, such as Variational Autoencoders (VAEs), Generative Adversarial Netwo...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Biomedical engineering letters Jg. 15; H. 5; S. 831 - 843
Hauptverfasser:	Cheong, Sehyoung, Lee, Hoseok, Kim, Won Hwa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Korea The Korean Society of Medical and Biological Engineering 01.09.2025 Springer Nature B.V
Schlagworte:	Biological and Medical Physics Biomedical Engineering and Bioengineering Biomedicine Biophysics Brain Brain research Conditioning Data acquisition Datasets Deep learning Diffusion models Disease Engineering Generative adversarial networks Genetic factors Image quality Learning Machine learning Medical and Radiation Physics Medical imaging Nervous system Neural networks Neuroimaging Neurosciences Phenotypes Review Article Synthetic data Variables Conditional generation Brain imaging VAE GAN Diffusion model
ISSN:	2093-9868, 2093-985X, 2093-985X
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	Generative models have become innovative tools across various domains, including neuroscience, where they enable the synthesis of realistic brain imaging data that captures complex anatomical and functional patterns. These models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, leverage deep learning to generate high-quality brain images while maintaining biological and clinical relevance. These models address critical challenges in brain imaging, e.g., the high cost and time required for data acquisition and the frequent imbalance in datasets, particularly for rare diseases or specific population groups. By conditioning the generative process on variables such as age, sex, clinical phenotypes, or genetic factors, these models enhance dataset diversity and provide opportunities to study underrepresented scenarios, model disease progression, and perform controlled experiments that are otherwise infeasible. Additionally, synthetic data generated by these models offer a potential solution to data privacy concerns, as they provide realistic non-identifiable data. As generative models continue to develop, they hold significant potential to substantially advance neuroscience by augmenting datasets, improving diagnostic accuracy, and accelerating the development of personalized treatments. This paper provides a comprehensive overview of the advancements in generative modeling techniques and their applications in brain imaging, with a particular emphasis on conditional generative methods. By categorizing existing approaches, addressing key challenges, and highlighting future directions, this paper aims to advance the integration of conditional generative models into neuroscience research and clinical workflows.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23
ISSN:	2093-9868 2093-985X 2093-985X
DOI:	10.1007/s13534-025-00487-3