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- W4306249615 abstract "Abstract One of the most critical problems in medical imaging is having high-quality data on healthy and sick patients. Also, gathering and creating a useful dataset is very time-consuming and is not always cost-effective. Machine learning methods are the newest methods in image processing, especially in medical image processing for classification, segmentation, and translation. GAN (Generative Adversarial Networks) is a class of machine learning frameworks that we consider a solution to image-to-image translation problems and augmentation. One of GAN's applications is generating more realistic data for training and validation to improve the performance of the algorithm and evaluation. In this paper, we propose a high-quality image-to-image translation framework based on CycleGAN in a paired and unpaired model of translation from T1 (or T2) to T2 (or T1) weighted MRI (Magnetic Resonance Imaging) of brain images. For evaluation, we used a dataset that consisted of T1 and T2 images acquired using the 3D structural MRI modality in four training and testing categories, which included 1113 structural MRI scans of large amounts of neuroimaging data." @default.
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- W4306249615 date "2022-10-14" @default.
- W4306249615 modified "2023-09-30" @default.
- W4306249615 title "Brain MRI Technics Images Translation by Generative Adversarial Network" @default.
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- W4306249615 doi "https://doi.org/10.21203/rs.3.rs-720998/v1" @default.
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