Generative Models For Large-Scale Simulations Of Connectome Development

Brooks, Skylar J; Stamoulis, Catherine

doi:10.1109/icasspw59220.2023.10193544

2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) 2023

DOI: 10.1109/icasspw59220.2023.10193544

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Generative Models For Large-Scale Simulations Of Connectome Development

Skylar J Brooks

Catherine Stamoulis

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2024

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Tools to Create Synthetic Data for Brain Images

Sindhu,

Vijayalakshmi

2024

Advances in Data Mining and Database Management

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In the areas of neuroscience, medical imaging, and machine learning, the creation of synthetic data for brain scans has become a key approach. This chapter explores the concept and significance of synthetic data generation for brain images. In tasks like brain picture segmentation, disease detection, and image analysis, machine learning models perform better when using synthetic data as a catalyst for data augmentation. A wide range of methods and resources including MRI simulators, 3D modeling software, deep learning frameworks, and medical imaging software are used to create synthetic brain images. To guarantee the validity and applicability of synthetic data, however, ethical issues, data representativeness, and transparency in the generation process continue to be essential factors. Synthetic brain data are becoming more useful and realistic as technology develops, and this has the potential to completely change the fields of neuroscience and medical imaging.

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Tools to Create Synthetic Data for Brain Images

Sindhu,

Vijayalakshmi

2024

Advances in Data Mining and Database Management

View full text Add to dashboard Cite

show abstract

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Generative Models For Large-Scale Simulations Of Connectome Development

Cited by 1 publication

References 18 publications

Tools to Create Synthetic Data for Brain Images

Tools to Create Synthetic Data for Brain Images

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