Results 231 to 240 of about 16,046 (295)

Unsupervised industrial image defect detection based on autoencoder and GANs. [PDF]

PLoS One
An S, Wu J, Li J.
europepmc   +1 more source

FMDNet: Spatial‐frequency feature routing for low‐dose CT denoising

Journal of Applied Clinical Medical Physics, Volume 27, Issue 6, June 2026.
Abstract Background Low‐dose computed tomography (LDCT) is widely used to reduce radiation exposure, but the reduced photon budget amplifies quantum noise and can introduce structured artifacts that obscure subtle boundaries and textures. Many deep learning denoisers process features in a single stream, which may encourage either over‐smoothing of weak
Yujie Yao, Yunsen Liang, Wen Xiao, Zibo Zhou, Yaoxue Xu, Yonghao Pan, Xiao Xia   +6 more
wiley   +1 more source

Mechanisms of Generative Image-to-Image Translation Networks. [PDF]

IEEE Access
Chen G, Sun M, Mao ZH, Liu K, Jia W.
europepmc   +1 more source

Design and validation of a deep learning GAN for predicting 177Lu dose voxel kernels using GATE/GEANT4 Monte Carlo and IDAC‐Dose 2.1 in phantom dosimetry

Journal of Applied Clinical Medical Physics, Volume 27, Issue 6, June 2026.
Abstract Background The rapid evolution of deep learning (DL), particularly the emergence of generative adversarial networks (GANs) has demonstrated strong potential for generating high‐resolution, voxel‐level dosimetric data. However, GAN‐based approaches have not yet been broadly applied to the generation of tissue‐specific dose voxel kernels (DVKs ...
Otieno Erick Kapis, Muchai Ian Kaniu, Iaccarino Giuseppe, Hudson Kalambuka Angeyo   +3 more
wiley   +1 more source

Artificial Intelligence (AI) in Computer-Aided Design (CAD): How AI Is Redefining Dental CAD. [PDF]

Cureus
Nassif J, Nassif W, Sabbagh J, Attarian R, Moustafa G.   +4 more
europepmc   +1 more source

Seeing Through Scattering With Computational Advances: A Review

Advanced Photonics Research, Volume 7, Issue 6, June 2026.
In scattering media, light scrambles into random speckles and impedes our vision. Unlocking hidden information enables breakthroughs to see behind the opaqueness, inspiring applications in imaging, communication, and encryption. Unlike clear media such as clear water and air, a scattering medium is inhomogeneous, in which propagating photons are ...
Huanhao Li, Kai Chen, Xueli Chen, Puxiang Lai, Zhipeng Yu   +4 more
wiley   +1 more source

Synthesis of Amyloid Images Using a Generative Adversarial Network from 2-Dimensional ¹⁸F-FDG Images and Evaluation for Clinical Use. [PDF]

J Nucl Med Technol
Honda M, Yamada T, Watanabe S, Watanabe A, Nagaoka T, Nemoto M, Mikami K, Hanaoka K, Kaida H, Handa H, Ishii K, Kimura Y.   +11 more
europepmc   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, Volume 13, Issue 34, 19 June 2026.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

Histopathological Assessment of Myocardial Ischemia-Reperfusion Injury Using Transformer-Based Artificial Intelligence: Model Comparison Study. [PDF]

JMIR Med Inform
Liu C, Xu M, Lv Y, Zhu Z, Pan Y, Wang Y.
europepmc   +1 more source

Domain‐Aware Implicit Network for Arbitrary‐Scale Remote Sensing Image Super‐Resolution

Advanced Intelligent Discovery, Volume 2, Issue 3, June 2026.
Although existing arbitrary‐scale image super‐resolution methods are flexible to reconstruct images with arbitrary scales, the characteristic of training distribution is neglected that there exists domain shift between samples of various scales. In this work, a Domain‐Aware Implicit Network (DAIN) is proposed to handle it from the perspective of domain
Xiaoxuan Ren, Qian Jiang, Xin Jin, Puming Wang, Xing Chu, Shaowen Yao, Shengfa Miao   +6 more
wiley   +1 more source