Generative adversarial networks in medical image augmentation: a review

Y Chen, XH Yang, Z Wei, AA Heidari, N Zheng… - Computers in Biology …, 2022 - Elsevier
Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …

MedGAN: An adaptive GAN approach for medical image generation

K Guo, J Chen, T Qiu, S Guo, T Luo, T Chen… - Computers in Biology …, 2023 - Elsevier
Generative adversarial networks (GANs) and their variants as an effective method for
generating visually appealing images have shown great potential in different medical …

[HTML][HTML] Left ventricle segmentation combining deep learning and deformable models with anatomical constraints

MAO Ribeiro, FLS Nunes - Journal of Biomedical Informatics, 2023 - Elsevier
Segmentation of the left ventricle is a key approach in Cardiac Magnetic Resonance
Imaging for calculating biomarkers in diagnosis. Since there is substantial effort required …

ROP-GAN: an image synthesis method for retinopathy of prematurity based on generative adversarial network

N Hou, J Shi, X Ding, C Nie, C Wang… - Physics in Medicine & …, 2023 - iopscience.iop.org
Objective. Training data with annotations are scarce in the intelligent diagnosis of
retinopathy of prematurity (ROP), and existing typical data augmentation methods cannot …

[PDF][PDF] Generative Adversarial Networks in Medical Image Augmentation: A

Y Chen, XH Yang, Z Wei, AA Heidari, N Zheng, Z Li - researchgate.net
Object: With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …