Results 81 to 90 of about 9,200 (172)

Plasma-CycleGAN: Plasma Biomarker-Guided MRI to Pet Cross-Modality Translation Using Conditional CycleGAN

2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI)
Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying patients and quantifying brain amyloid levels.
Chen, Yanxi, Su, Yi, Dumitrascu, Celine, Chen, Kewei, Weidman, David, Caselli, Richard J, Ashton, Nicholas, Reiman, Eric M, Wang, Yalin   +8 more
openaire   +2 more sources

Detecting Deepfakes Using Two‐Stream Framework Based on EfficientNet and Vision Transformer

Journal of Electrical and Computer Engineering, Volume 2026, Issue 1, 2026.
The ever‐growing threat of deepfake technology has stimulated research in deepfake forensics to identify the genuineness of media materials on the Internet. The majority of existing techniques employ the convolutional neural network (CNN) framework to identify deepfake by capturing the image’s local features.
Qiang Yang, Yijun Cai, Rong Yan, Guangbin Zhang, Gongping Yang   +4 more
wiley   +1 more source

LM-CycleGAN: Improving Underwater Image Quality Through Learned Perceptual Image Patch Similarity and Multi-Scale Adaptive Fusion Attention

Sensors
The underwater imaging process is often hindered by high noise levels, blurring, and color distortion due to light scattering, absorption, and suspended particles in the water.
Jiangyan Wu, Guanghui Zhang, Yugang Fan
doaj   +1 more source

mpMRI‐based MGMT methylation status prediction for glioblastoma through off‐the‐shelf deep features: A multi‐dataset feasibility study

Journal of Applied Clinical Medical Physics, Volume 26, Issue 12, December 2025.
Abstract Objectives O‐6‐methylguanine DNA methyltransferase (MGMT) promoter methylation status is a critical prognostic factor in glioblastoma. The aim of this study is to evaluate the feasibility of diagnosing MGMT status in a rapid, non‐invasive manner using multiparametric magnetic resonance imaging (mpMRI).
Junhua Chen, Zhanghong Wang, Banghua Yang   +2 more
wiley   +1 more source

Improving Accuracy of IC Surface Defects Detection via Enhanced-CycleGAN Data Augmentation

IEEE Access
An important step in integrated circuit (IC) manufacturing is inspection of the chip surface for defects. In practice, there exists a data imbalance problem associated with IC surface defect images which affects the detection performance of a deep ...
Lamia Alam, Nasser Kehtarnavaz
doaj   +1 more source

Transfer Learning and Permutation‐Invariance Improving Predicting Genome‐Wide, Cell‐Specific and Directional Interventions Effects of Complex Systems

Advanced Science, Volume 12, Issue 46, December 11, 2025.
SETComp, a transfer learning model based on permutation‐invariance, is pre‐trained on single‐compound intervention data and fine‐tuned on complex system (e.g., natural products) data. The model achieves up to 93.86% accuracy on complex system‐cell‐gene association predictions, outperforming the baseline by up to 27.59%.
Boyang Wang, Boyu Pan, Tingyu Zhang, Qingyuan Liu, Shao Li   +4 more
wiley   +1 more source

The Optimization of Illustration Design in Cultural and Creative Products for Liaoning Region Under Intelligent Generative Adversarial Network

IEEE Access
This study explores the application of intelligent Generative Adversarial Networks (GANs) in illustration design and cultural and creative product design in Liaoning.
Minghui Niu, Ying Zhou
doaj   +1 more source

Adaptive Weighted Multi-Discriminator CycleGAN for Underwater Image Enhancement

Journal of Marine Science and Engineering, 2019
In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but
Jaihyun Park, David K. Han, Hanseok Ko
doaj   +1 more source

Unsupervised Deep Learning for Synthetic CT Generation from CBCT Images for Proton and Carbon Ion Therapy for Paediatric Patients

Sensors
Image-guided treatment adaptation is a game changer in oncological particle therapy (PT), especially for younger patients. The purpose of this study is to present a cycle generative adversarial network (CycleGAN)-based method for synthetic computed ...
Matteo Pepa, Siavash Taleghani, Giulia Sellaro, Alfredo Mirandola, Francesca Colombo, Sabina Vennarini, Mario Ciocca, Chiara Paganelli, Ester Orlandi, Guido Baroni, Andrea Pella   +10 more
doaj   +1 more source

CycleGAN with Better Cycles

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.
Wang, Tongzhou, Lin, Yihan
openaire   +2 more sources