Crossing-Domain Generative Adversarial Networks for Unsupervised Multi-Domain Image-to-Image Translation [PDF]
Xuewen Yang, Dongliang Xie, Xin Wang
openalex +1 more source
The integration of foundation models into computational microscopy revolutionizes biomedical research by enhancing imaging resolution, accelerating data analysis, and enabling real‐time biological interpretation. This systematic review critically examines recent advancements, highlights translational challenges, and discusses the transformative ...
Di Ding +5 more
wiley +1 more source
Unsupervised anomaly detection for gearboxes based on the deep convolutional support generative adversarial network. [PDF]
Zhang C, Guo Z, Li C.
europepmc +1 more source
Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
Yuan Yuan +5 more
openalex +2 more sources
On‐Device Brain Tumor Classification from MR Images Using Smartphone
Herein, various deep learning models are trained for brain tumor classification task, and model performances are compared. The performance is further improved by using the proposed preprocessing algorithm before training. The MobileViT model, which is the best‐performing model in terms of balance between inference time and success rate, is integrated ...
Halil Ibrahim Ustun +3 more
wiley +1 more source
Dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT for MR-guided adaptive radiation therapy. [PDF]
Asher GL +10 more
europepmc +1 more source
Exploring the Potential of Generative Adversarial Networks for Synthesizing Radiological Images of the Spine to be Used in In Silico Trials [PDF]
Fabio Galbusera +6 more
openalex +1 more source
This study addresses multiagent defense challenges against low‐cost swarm attacks through a hierarchical framework combining resource allocation and PPO optimization. The three‐layer architecture coordinates strategic planning, deployment optimization, and real‐time execution.
Xiaokai Fei +2 more
wiley +1 more source
Tabular transformer generative adversarial network for heterogeneous distribution in healthcare. [PDF]
Kang HYJ, Ko M, Ryu KS.
europepmc +1 more source

