Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis
Ultrasound (US) imaging is widely used for anatomical structure inspection in clinical
diagnosis. The training of new sonographers and deep learning based algorithms for US …
diagnosis. The training of new sonographers and deep learning based algorithms for US …
Image quality improvement of hand-held ultrasound devices with a two-stage generative adversarial network
As a widely used imaging modality in the medical field, ultrasound has been applied in
community medicine, rural medicine, and even telemedicine in recent years. Therefore, the …
community medicine, rural medicine, and even telemedicine in recent years. Therefore, the …
SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing
L Bargsten, A Schlaefer - … journal of computer assisted radiology and …, 2020 - Springer
Purpose In the field of medical image analysis, deep learning methods gained huge
attention over the last years. This can be explained by their often improved performance …
attention over the last years. This can be explained by their often improved performance …
Gans for medical image synthesis: An empirical study
Generative adversarial networks (GANs) have become increasingly powerful, generating
mind-blowing photorealistic images that mimic the content of datasets they have been …
mind-blowing photorealistic images that mimic the content of datasets they have been …
Breast ultrasound image synthesis using deep convolutional generative adversarial networks
T Fujioka, M Mori, K Kubota, Y Kikuchi, L Katsuta… - Diagnostics, 2019 - mdpi.com
Deep convolutional generative adversarial networks (DCGANs) are newly developed tools
for generating synthesized images. To determine the clinical utility of synthesized images …
for generating synthesized images. To determine the clinical utility of synthesized images …
Simulating patho-realistic ultrasound images using deep generative networks with adversarial learning
Ultrasound imaging makes use of backscattering of waves during their interaction with
scatterers present in biological tissues. Simulation of synthetic ultrasound images is a …
scatterers present in biological tissues. Simulation of synthetic ultrasound images is a …
Freehand ultrasound image simulation with spatially-conditioned generative adversarial networks
Sonography synthesis has a wide range of applications, including medical procedure
simulation, clinical training and multimodality image registration. In this paper, we propose a …
simulation, clinical training and multimodality image registration. In this paper, we propose a …
SkrGAN: Sketching-rendering unconditional generative adversarial networks for medical image synthesis
Abstract Generative Adversarial Networks (GANs) have the capability of synthesizing
images, which have been successfully applied to medical image synthesis tasks. However …
images, which have been successfully applied to medical image synthesis tasks. However …
Medical image synthesis with deep convolutional adversarial networks
Medical imaging plays a critical role in various clinical applications. However, due to
multiple considerations such as cost and radiation dose, the acquisition of certain image …
multiple considerations such as cost and radiation dose, the acquisition of certain image …
High-resolution mammogram synthesis using progressive generative adversarial networks
D Korkinof, T Rijken, M O'Neill, J Yearsley… - arXiv preprint arXiv …, 2018 - arxiv.org
The ability to generate synthetic medical images is useful for data augmentation, domain
transfer, and out-of-distribution detection. However, generating realistic, high-resolution …
transfer, and out-of-distribution detection. However, generating realistic, high-resolution …