Results 61 to 70 of about 2,911,065 (235)

Applications of generative adversarial networks in neuroimaging and clinical neuroscience

NeuroImage, 2023
Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a ...
Rongguang Wang, Vishnu Bashyam, Zhijian Yang, Fanyang Yu, Vasiliki Tassopoulou, Sai Spandana Chintapalli, Ioanna Skampardoni, Lasya P. Sreepada, Dushyant Sahoo, Konstantina Nikita, Ahmed Abdulkadir, Junhao Wen, Christos Davatzikos   +12 more
doaj   +1 more source

Convergence Problems with Generative Adversarial Networks (GANs)

CoRR, 2018
47 pages, 4 ...
openaire   +2 more sources

KG-GAN: Knowledge-Guided Generative Adversarial Networks

CoRR, 2019
Can generative adversarial networks (GANs) generate roses of various colors given only roses of red petals as input? The answer is negative, since GANs' discriminator would reject all roses of unseen petal colors. In this study, we propose knowledge-guided GAN (KG-GAN) to fuse domain knowledge with the GAN framework.
Che-Han Chang, Chun-Hsien Yu, Szu-Ying Chen, Edward Y. Chang   +3 more
openaire   +2 more sources

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

Advanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li, Kelly Yichen Li, Chiara Nicoletti, Stephen Kwok‐Wing Tsui, Pier Lorenzo Puri, Qin Cao, Kevin Y. Yip   +6 more
wiley   +1 more source

A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials

Biomedicines
Machine learning has transformed ophthalmology, particularly in predictive and discriminatory models for vitreoretinal pathologies. However, generative modeling, especially generative adversarial networks (GANs), remains underexplored.
Raheem Remtulla, Adam Samet, Merve Kulbay, Arjin Akdag, Adam Hocini, Anton Volniansky, Shigufa Kahn Ali, Cynthia X. Qian   +7 more
doaj   +1 more source

Deepfakes in Ophthalmology

Ophthalmology Science, 2021
Purpose: Generative adversarial networks (GANs) are deep learning (DL) models that can create and modify realistic-appearing synthetic images, or deepfakes, from real images.
Jimmy S. Chen, MD, Aaron S. Coyner, PhD, R.V. Paul Chan, MD, M. Elizabeth Hartnett, MD, Darius M. Moshfeghi, MD, Leah A. Owen, MD, PhD, Jayashree Kalpathy-Cramer, PhD, Michael F. Chiang, MD, MA, J. Peter Campbell, MD, MPH   +8 more
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Defect Enhancement Generative Adversarial Network for Enlarging Data Set of Microcrack Defect

IEEE Access, 2019
This paper presents a micro defect data set expansion method focuses on the microcrack defect of magnetic ring. Deep neural networks require a mass of training samples to be fully optimized.
Song Lin, Zhiyong He, Lining Sun
doaj   +1 more source

SRV-GAN: A generative adversarial network for segmenting retinal vessels

Mathematical Biosciences and Engineering, 2022
<abstract> <p>In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs)
Chen Yue, Mingquan Ye, Peipei Wang, Daobin Huang, Xiaojie Lu   +4 more
openaire   +3 more sources

Generative adversarial networks: an overview [PDF]

, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this by deriving backpropagation signals through a competitive process involving a pair of networks.
Antonia Creswell, Kai Arulkumaran, Creswell, Antonia, Anil A. Bharath, Biswa Sengupta, Arulkumaran, Kai, While, Tom, Dumoulin, Vincent, Sengupta, Biswa, Bharath, Anil A, Tom White, Vincent Dumoulin   +11 more
core   +1 more source