144 related articles for article (PubMed ID: 36302815)
1. Automation of generative adversarial network-based synthetic data-augmentation for maximizing the diagnostic performance with paranasal imaging.
Kong HJ; Kim JY; Moon HM; Park HC; Kim JW; Lim R; Woo J; Fakhri GE; Kim DW; Kim S
Sci Rep; 2022 Oct; 12(1):18118. PubMed ID: 36302815
[TBL] [Abstract][Full Text] [Related]
2. Transfer learning in diagnosis of maxillary sinusitis using panoramic radiography and conventional radiography.
Kotaki S; Nishiguchi T; Araragi M; Akiyama H; Fukuda M; Ariji E; Ariji Y
Oral Radiol; 2023 Jul; 39(3):467-474. PubMed ID: 36166134
[TBL] [Abstract][Full Text] [Related]
3. W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT.
Park C; Kang JW; Lee DE; Son W; Lee SM; Park C; Kim M
Comput Med Imaging Graph; 2024 Jul; 115():102387. PubMed ID: 38703602
[TBL] [Abstract][Full Text] [Related]
4. Generative Adversarial Network for Medical Images (MI-GAN).
Iqbal T; Ali H
J Med Syst; 2018 Oct; 42(11):231. PubMed ID: 30315368
[TBL] [Abstract][Full Text] [Related]
5. Deep Learning in Diagnosis of Maxillary Sinusitis Using Conventional Radiography.
Kim Y; Lee KJ; Sunwoo L; Choi D; Nam CM; Cho J; Kim J; Bae YJ; Yoo RE; Choi BS; Jung C; Kim JH
Invest Radiol; 2019 Jan; 54(1):7-15. PubMed ID: 30067607
[TBL] [Abstract][Full Text] [Related]
6. 3-To-1 Pipeline: Restructuring Transfer Learning Pipelines for Medical Imaging Classification via Optimized GAN Synthetic Images.
Jian Choong RZ; Austin Harding S; Tang BY; Liao SW
Annu Int Conf IEEE Eng Med Biol Soc; 2020 Jul; 2020():1596-1599. PubMed ID: 33018299
[TBL] [Abstract][Full Text] [Related]
7. Data augmentation for enhancing EEG-based emotion recognition with deep generative models.
Luo Y; Zhu LZ; Wan ZY; Lu BL
J Neural Eng; 2020 Oct; 17(5):056021. PubMed ID: 33052888
[TBL] [Abstract][Full Text] [Related]
8. Deep Learning for Diagnosis of Paranasal Sinusitis Using Multi-View Radiographs.
Jeon Y; Lee K; Sunwoo L; Choi D; Oh DY; Lee KJ; Kim Y; Kim JW; Cho SJ; Baik SH; Yoo RE; Bae YJ; Choi BS; Jung C; Kim JH
Diagnostics (Basel); 2021 Feb; 11(2):. PubMed ID: 33562764
[TBL] [Abstract][Full Text] [Related]
9. Active Appearance Model Induced Generative Adversarial Network for Controlled Data Augmentation.
Liu J; Shen C; Liu T; Aguilera N; Tam J
Med Image Comput Comput Assist Interv; 2019 Oct; 11764():201-208. PubMed ID: 31696163
[TBL] [Abstract][Full Text] [Related]
10. Data Augmentation for Deep-Learning-Based Multiclass Structural Damage Detection Using Limited Information.
Dunphy K; Fekri MN; Grolinger K; Sadhu A
Sensors (Basel); 2022 Aug; 22(16):. PubMed ID: 36015955
[TBL] [Abstract][Full Text] [Related]
11. Generative approach for data augmentation for deep learning-based bone surface segmentation from ultrasound images.
Zaman A; Park SH; Bang H; Park CW; Park I; Joung S
Int J Comput Assist Radiol Surg; 2020 Jun; 15(6):931-941. PubMed ID: 32399586
[TBL] [Abstract][Full Text] [Related]
12. Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks.
Sandfort V; Yan K; Pickhardt PJ; Summers RM
Sci Rep; 2019 Nov; 9(1):16884. PubMed ID: 31729403
[TBL] [Abstract][Full Text] [Related]
13. A Generative Adversarial Network to Synthesize 3D Magnetohydrodynamic Distortions for Electrocardiogram Analyses Applied to Cardiac Magnetic Resonance Imaging.
Mehri M; Calmon G; Odille F; Oster J; Lalande A
Sensors (Basel); 2023 Oct; 23(21):. PubMed ID: 37960391
[TBL] [Abstract][Full Text] [Related]
14. Multichannel One-Dimensional Data Augmentation with Generative Adversarial Network.
Kosasih DI; Lee BG; Lim H
Sensors (Basel); 2023 Sep; 23(18):. PubMed ID: 37765750
[TBL] [Abstract][Full Text] [Related]
15. High-content image generation for drug discovery using generative adversarial networks.
Hussain S; Anees A; Das A; Nguyen BP; Marzuki M; Lin S; Wright G; Singhal A
Neural Netw; 2020 Dec; 132():353-363. PubMed ID: 32977280
[TBL] [Abstract][Full Text] [Related]
16. Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review.
Ali H; Shah Z
JMIR Med Inform; 2022 Jun; 10(6):e37365. PubMed ID: 35709336
[TBL] [Abstract][Full Text] [Related]
17. Data Augmentation of Backscatter X-ray Images for Deep Learning-Based Automatic Cargo Inspection.
Cho H; Park H; Kim IJ; Cho J
Sensors (Basel); 2021 Nov; 21(21):. PubMed ID: 34770600
[TBL] [Abstract][Full Text] [Related]
18. Improving effectiveness of different deep learning-based models for detecting COVID-19 from computed tomography (CT) images.
Acar E; Şahin E; Yılmaz İ
Neural Comput Appl; 2021; 33(24):17589-17609. PubMed ID: 34345118
[TBL] [Abstract][Full Text] [Related]
19. iEnhancer-GAN: A Deep Learning Framework in Combination with Word Embedding and Sequence Generative Adversarial Net to Identify Enhancers and Their Strength.
Yang R; Wu F; Zhang C; Zhang L
Int J Mol Sci; 2021 Mar; 22(7):. PubMed ID: 33808317
[TBL] [Abstract][Full Text] [Related]
20. Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder.
Carrle FP; Hollenbenders Y; Reichenbach A
Front Neurosci; 2023; 17():1219133. PubMed ID: 37849893
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
