Results 91 to 100 of about 60,338 (274)

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Transformer-based conditional generative transfer learning network for cross domain fault diagnosis under limited data

Scientific Reports
Most current generative adversarial network (GAN) cannot simultaneously consider the quality and diversity of generated samples due to limited data and variable working condition.
Xiang Li, Jun Ma, Jiande Wu, Zhuorui Li, Zhengzhou Tan   +4 more
doaj   +1 more source

Using a Visual Turing Test to Evaluate the Realism of Generative Adversarial Network (GAN)-Based Synthesized Myocardial Perfusion Images. [PDF]

Cureus, 2022
Higaki A, Kawada Y, Hiasa G, Yamada T, Okayama H.   +4 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing

Machines
In this study, we present the few-shot generative adversarial network (FS-GAN) model, which integrates few-shot learning and a generative adversarial network with an unsupervised learning approach (AnoGAN) to address the challenges of anomaly detection ...
Tae-yong Kim, Jieun Lee, Seokhyun Gong, Jaehoon Lim, Dowan Kim, Jongpil Jeong   +5 more
doaj   +1 more source

Improving synthetic media generation and detection using generative adversarial networks [PDF]

PeerJ Computer Science
Synthetic images ar­­­e created using computer graphics modeling and artificial intelligence techniques, referred to as deepfakes. They modify human features by using generative models and deep learning algorithms, posing risks violations of social media
Rabbia Zia, Mariam Rehman, Afzaal Hussain, Shahbaz Nazeer, Maria Anjum   +4 more
doaj   +2 more sources

Automatic Hemorrhage Detection From Color Doppler Ultrasound Using a Generative Adversarial Network (GAN)-Based Anomaly Detection Method. [PDF]

IEEE J Transl Eng Health Med, 2022
Mitra J, Qiu J, MacDonald M, Venugopal P, Wallace K, Abdou H, Richmond M, Elansary N, Edwards J, Patel N, Morrison J, Marinelli L.   +11 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data. [PDF]

Sensors (Basel), 2021
Vaccari I, Orani V, Paglialonga A, Cambiaso E, Mongelli M.   +4 more
europepmc   +1 more source

Video Gaze Redirection using Generative Adversarial Network (GAN)

International Journal of Computing and Digital Systems, 2022
Sikha Suni, Sneha Johnson, Treasa Mani, Vishak J Nair, Anjusree V.K   +4 more
openaire   +1 more source