Free-form tumor synthesis in computed tomography images via richer generative adversarial network
The insufficiency of annotated medical imaging scans for cancer makes it challenging to
train and validate data-hungry deep learning models in precision oncology. We propose a …
train and validate data-hungry deep learning models in precision oncology. We propose a …
3DGAUnet: 3D generative adversarial networks with a 3D U-net based generator to achieve the accurate and effective synthesis of clinical tumor image data for …
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) has the most elevated fatality
rate among the primary types of solid malignancies, posing an urgent need for early …
rate among the primary types of solid malignancies, posing an urgent need for early …
A feature invariant generative adversarial network for head and neck MRI/CT image synthesis
With the emergence of online MRI radiotherapy treatments, MR-based workflows have
increased in importance in the clinical workflow. However proper dose planning still requires …
increased in importance in the clinical workflow. However proper dose planning still requires …
Attention-guided generative adversarial network to address atypical anatomy in synthetic CT generation
Recently, interest in MR-only treatment planning using synthetic CTs (synCTs) has grown
rapidly in radiation therapy. However, developing class solutions for medical images that …
rapidly in radiation therapy. However, developing class solutions for medical images that …
Lung cancer CT image generation from a free-form sketch using style-based pix2pix for data augmentation
R Toda, A Teramoto, M Kondo, K Imaizumi, K Saito… - Scientific reports, 2022 - nature.com
Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in
collecting and using large datasets. One method proposed for solving this problem is data …
collecting and using large datasets. One method proposed for solving this problem is data …
[HTML][HTML] Realistic high-resolution body computed tomography image synthesis by using progressive growing generative adversarial network: visual turing test
HY Park, HJ Bae, GS Hong, M Kim… - JMIR medical …, 2021 - medinform.jmir.org
Background: Generative adversarial network (GAN)–based synthetic images can be viable
solutions to current supervised deep learning challenges. However, generating highly …
solutions to current supervised deep learning challenges. However, generating highly …
LEGAN: A Light and Effective Generative Adversarial Network for medical image synthesis
J Gao, W Zhao, P Li, W Huang, Z Chen - Computers in Biology and …, 2022 - Elsevier
Medical image synthesis plays an important role in clinical diagnosis by providing auxiliary
pathological information. However, previous methods usually utilize the one-step strategy …
pathological information. However, previous methods usually utilize the one-step strategy …
Medical image generation using generative adversarial networks
Generative adversarial networks (GANs) are unsupervised Deep Learning approach in the
computer vision community which has gained significant attention from the last few years in …
computer vision community which has gained significant attention from the last few years in …
Deep generative model for synthetic-CT generation with uncertainty predictions
MR-only radiation treatment planning is attractive due to the superior soft tissue definition of
MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI …
MRI as compared to CT, and the elimination of the uncertainty introduced by CT-MRI …
3D-StyleGAN: A style-based generative adversarial network for generative modeling of three-dimensional medical images
Abstract Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional
(3D) medical images has great potential that can be extended to many medical applications …
(3D) medical images has great potential that can be extended to many medical applications …