Results 41 to 50 of about 5,457 (203)

Comprehensive evaluation of similarity between synthetic and real CT images for nasopharyngeal carcinoma

Radiation Oncology, 2023
Background Although magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis studies based on deep learning have significantly progressed, the similarity between synthetic CT (sCT) and real CT (rCT) has only been evaluated in image quality ...
Siqi Yuan, Xinyuan Chen, Yuxiang Liu, Ji Zhu, Kuo Men, Jianrong Dai   +5 more
doaj   +1 more source

Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy

Frontiers in Oncology, 2021
PurposeTo propose a synthesis method of pseudo-CT (CTCycleGAN) images based on an improved 3D cycle generative adversarial network (CycleGAN) to solve the limitations of cone-beam CT (CBCT), which cannot be directly applied to the correction of ...
Hongfei Sun, Rongbo Fan, Chunying Li, Chunying Li, Chunying Li, Zhengda Lu, Zhengda Lu, Zhengda Lu, Kai Xie, Kai Xie, Kai Xie, Xinye Ni, Xinye Ni, Xinye Ni, Jianhua Yang   +14 more
doaj   +1 more source

Innovative paths of CycleGAN loss functions and evolution of cross-domain applications [PDF]

ITM Web of Conferences
Image translation is one of the most popular topics in computer vision. The development of CycleGAN addresses several issues in multi- task applications, including image translation.
Liu Lijia
doaj   +1 more source

CycleGAN Models for MRI Image Translation

CoRR, 2023
Accepted and presented in ACML PRHA 2023 ...
Cassandra Czobit, Reza Samavi
openaire   +2 more sources

Residual cyclegan for robust domain transformation of histopathological tissue slides

, 2021
Variation between stains in histopathology is commonplace across different medical centers. This can have a significant effect on the reliability of machine learning algorithms.
Bokhorst, John-Melle,, Bokhorst, John-Melle, de Bel, Thomas, Laak, J.A.W.M. van der, van der Laak, Jeroen,, Litjens, Geert, Bel, T. de, Bokhorst, J.M., van der Laak, Jeroen, Litjens, G.J.S., de Bel, Thomas,, Litjens, Geert,, John-Melle Bokhorst, Jeroen van der Laak, Thomas de Bel, Geert Litjens   +15 more
core   +1 more source

Enhancing Negative Film Colorization through Systematic CycleGAN Architectural Modifications: A Comprehensive Analysis of Generator and Discriminator Performance

Journal of Applied Informatics and Computing
This research addresses the urgent need for deep learning-based negative film colorization technology through systematic modifications to the CycleGAN architecture.
Khaulyca Arva Artemysia Khaulyca, Arief Suryadi Satyawan Arief, Mokhammad Mirza Etnisa Haqiqi Mirza, Helfy Susilawati Helfy, Beni Wijaya Beni, Sani Moch Sopian Sani, Muhammad Ikbal Shamie Ikbal, Firman Firman   +7 more
doaj   +1 more source

ArCycleGAN: Improved CycleGAN for Style Transferring of Fruit Images

IEEE Access, 2021
CycleGAN can realize image translation and style transferring among unpaired images. However, it will easily generate inappropriate image results when the number and shapes of objects in the style offering image and the source image are greatly different.
Hongqian Chen, Mengxi Guan, Hui Li
doaj   +1 more source

Manifold fitting with CycleGAN

Proceedings of the National Academy of Sciences
Manifold fitting, which offers substantial potential for efficient and accurate modeling, poses a critical challenge in nonlinear data analysis. This study presents an approach that employs neural networks to fit the latent manifold. Leveraging the generative adversarial framework, this method learns smooth mappings between low-dimensional latent space
Zhigang Yao, Jiaji Su, Shing-Tung Yau
openaire   +2 more sources

CycleGANAS: Differentiable Neural Architecture Search for CycleGAN [PDF]

, 2023
We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the ...
Joo, Changhee, An, Taegun
core  

CycleGAN with Better Cycles

CoRR
CycleGAN provides a framework to train image-to-image translation with unpaired datasets using cycle consistency loss [4]. While results are great in many applications, the pixel level cycle consistency can potentially be problematic and causes unrealistic images in certain cases.
Tongzhou Wang 0001, Yihan Lin
openaire   +2 more sources