GAN-based synthetic brain MR image generation

By Changhee Han, Hideaki Hayashi, Leonardo Rundo, Ryosuke Araki, Wataru Shimoda, S. Muramatsu, Yujiro Furukawa, Giancarlo Mauri, Hideki Nakayama. IEEE International Symposium on Biomedical Imaging, 2018

360 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3f95b6ff3fda46db7fae313f5412759b86da0341

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In Biophysics

In Computer Vision and Pattern Recognition

Citations in the corpus

Generative Adversarial Network in Medical Imaging: A Review (2018, methodology): “(2017b) Semi-Coupled-GAN – M23 [Heart], Two generators coupled with a single discriminator which outputted both a distribution over the image data source and class labels Han et al. (2018) WGAN D17 M1 [Brain] Beers et al. (2018) PGGAN D18 [Brain] Bermudez et al. (2018) DCGAN D19 M1 [Brain]”
Infinite Brain MR Images: PGGAN-based Data Augmentation for Tumor Detection (2019, positive): “ modality for soft-tissue acquisition, has not yet been reported due to the diculties from low-contrast MR images, strong anatomical consistency, and intra-sequence variability; in our previous work [13], we generated 64 64/128 128 MR images using conventional GANs and even an expert physician faile”
Synthetic Data for Deep Learning (2019, background): “, lung nodule samples generated in [120] or magnetic resonance (MR) images of the brain in [82, 227].”
Synthetic data generation for tabular health records: A systematic review (2022, background): “Within healthcare, SDG has been researched for di ff erent modalities including biomedical signals [17], medical images [18], electronic health records (EHR) free-text content [19], time-series smart-home activity data [20] and EHR tabular data [21].”
Learning More with Less: Conditional PGGAN-based Data Augmentation for Brain Metastases Detection Using Highly-Rough Annotation on MR Images (2019, neutral): “Thissimilarlyappliestomedicalimagesinaclinicalenvironment, whereinphysicians’specialtyiscritical[6,10].”
A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, background): “GANs have been applied to a wide range of problems, apart from CI problems which include synthetic brain medical image generation [250], text-to-image generation [251], and personal image generation [252].”
Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks (2018, background): “els for multi-contrast MRI synthesis [62], [73], [74].”