Published on in Vol 9 (2024)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/58911, first published
.
Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss
Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss
Journals
- Zuo W, Bai J, Wei J, Long C. Fishbone ingestion is a non-negligible cause of intestinal perforation. Frontiers in Medicine 2025;12 View
- Du S, Shen M, Liu Y, Wei F, Lei Y. Development and evaluation of a novel organ-specific biomechanics-guided contrast-enhanced CT volume synthesis model*. Physics in Medicine & Biology 2025;70(21):215018 View
- Liu L, Chen L, Wang A. Innovative paths of CycleGAN loss functions and evolution of cross-domain applications. ITM Web of Conferences 2025;80:01014 View
- Malagila Raphael Y, Monkam P, Qi S, Liu S, He D, Qian W. Deep generative models in echocardiography: a scoping review of trends and future perspectives. Measurement Science and Technology 2026;37(4):042001 View
- Giourga M, Petropoulos I, Stavros S, Potiris A, Goula K, Moustakli E, Papahliou A, Daskalaki M, Segou M, Rodolakis A, Daskalakis G, Domali E. A Deep Learning Approach for Classifying Benign, Malignant, and Borderline Ovarian Tumors Using Convolutional Neural Networks and Generative Adversarial Networks. Medical Sciences 2026;14(1):89 View
- Kawattikul K, Hamamoto K. Toward Realistic Synthetic Ultrasound Images With Pose Vectors. IEEE Access 2026;14:42132 View
- Muñoz B, Pezoa R, Gutierrez H. HistoXplaining in Style: Counterfactual Explanations for Histopathology Images using StyleGAN2-ADA. Information Systems Frontiers 2026 View