129 related articles for article (PubMed ID: 33268370)
1. Breaking medical data sharing boundaries by using synthesized radiographs.
Han T; Nebelung S; Haarburger C; Horst N; Reinartz S; Merhof D; Kiessling F; Schulz V; Truhn D
Sci Adv; 2020 Dec; 6(49):. PubMed ID: 33268370
[TBL] [Abstract][Full Text] [Related]
2. Challenges in Implementing the Local Node Infrastructure for a National Federated Machine Learning Network in Radiology.
Jacobs PP; Ehrengut C; Bucher AM; Penzkofer T; Lukas M; Kleesiek J; Denecke T
Healthcare (Basel); 2023 Aug; 11(17):. PubMed ID: 37685411
[TBL] [Abstract][Full Text] [Related]
3. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.
Wu JT; Wong KCL; Gur Y; Ansari N; Karargyris A; Sharma A; Morris M; Saboury B; Ahmad H; Boyko O; Syed A; Jadhav A; Wang H; Pillai A; Kashyap S; Moradi M; Syeda-Mahmood T
JAMA Netw Open; 2020 Oct; 3(10):e2022779. PubMed ID: 33034642
[TBL] [Abstract][Full Text] [Related]
4. Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals.
Peng L; Luo G; Walker A; Zaiman Z; Jones EK; Gupta H; Kersten K; Burns JL; Harle CA; Magoc T; Shickel B; Steenburg SD; Loftus T; Melton GB; Gichoya JW; Sun J; Tignanelli CJ
J Am Med Inform Assoc; 2022 Dec; 30(1):54-63. PubMed ID: 36214629
[TBL] [Abstract][Full Text] [Related]
5. The future of Cochrane Neonatal.
Soll RF; Ovelman C; McGuire W
Early Hum Dev; 2020 Nov; 150():105191. PubMed ID: 33036834
[TBL] [Abstract][Full Text] [Related]
6. SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging.
Zhang M; Qu L; Singh P; Kalpathy-Cramer J; Rubin DL
IEEE J Biomed Health Inform; 2022 Sep; 26(9):4635-4644. PubMed ID: 35749336
[TBL] [Abstract][Full Text] [Related]
7. On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.
Segebarth D; Griebel M; Stein N; von Collenberg CR; Martin C; Fiedler D; Comeras LB; Sah A; Schoeffler V; Lüffe T; Dürr A; Gupta R; Sasi M; Lillesaar C; Lange MD; Tasan RO; Singewald N; Pape HC; Flath CM; Blum R
Elife; 2020 Oct; 9():. PubMed ID: 33074102
[TBL] [Abstract][Full Text] [Related]
8. Creating High Fidelity Synthetic Pelvis Radiographs Using Generative Adversarial Networks: Unlocking the Potential of Deep Learning Models Without Patient Privacy Concerns.
Khosravi B; Rouzrokh P; Mickley JP; Faghani S; Larson AN; Garner HW; Howe BM; Erickson BJ; Taunton MJ; Wyles CC
J Arthroplasty; 2023 Oct; 38(10):2037-2043.e1. PubMed ID: 36535448
[TBL] [Abstract][Full Text] [Related]
9. Demonstrating the successful application of synthetic learning in spine surgery for training multi-center models with increased patient privacy.
Schonfeld E; Veeravagu A
Sci Rep; 2023 Aug; 13(1):12481. PubMed ID: 37528216
[TBL] [Abstract][Full Text] [Related]
10. A multicenter random forest model for effective prognosis prediction in collaborative clinical research network.
Li J; Tian Y; Zhu Y; Zhou T; Li J; Ding K; Li J
Artif Intell Med; 2020 Mar; 103():101814. PubMed ID: 32143809
[TBL] [Abstract][Full Text] [Related]
11. Multi-site fMRI analysis using privacy-preserving federated learning and domain adaptation: ABIDE results.
Li X; Gu Y; Dvornek N; Staib LH; Ventola P; Duncan JS
Med Image Anal; 2020 Oct; 65():101765. PubMed ID: 32679533
[TBL] [Abstract][Full Text] [Related]
12. Assessment of Deep Generative Models for High-Resolution Synthetic Retinal Image Generation of Age-Related Macular Degeneration.
Burlina PM; Joshi N; Pacheco KD; Liu TYA; Bressler NM
JAMA Ophthalmol; 2019 Mar; 137(3):258-264. PubMed ID: 30629091
[TBL] [Abstract][Full Text] [Related]
13. Aggregating intrinsic information to enhance BCI performance through federated learning.
Liu R; Chen Y; Li A; Ding Y; Yu H; Guan C
Neural Netw; 2024 Apr; 172():106100. PubMed ID: 38232427
[TBL] [Abstract][Full Text] [Related]
14. Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.
Usman Akbar M; Larsson M; Blystad I; Eklund A
Sci Data; 2024 Feb; 11(1):259. PubMed ID: 38424097
[TBL] [Abstract][Full Text] [Related]
15. SinGAN-Seg: Synthetic training data generation for medical image segmentation.
Thambawita V; Salehi P; Sheshkal SA; Hicks SA; Hammer HL; Parasa S; Lange T; Halvorsen P; Riegler MA
PLoS One; 2022; 17(5):e0267976. PubMed ID: 35500005
[TBL] [Abstract][Full Text] [Related]
16. Evaluation of Generative Adversarial Networks for High-Resolution Synthetic Image Generation of Circumpapillary Optical Coherence Tomography Images for Glaucoma.
Sreejith Kumar AJ; Chong RS; Crowston JG; Chua J; Bujor I; Husain R; Vithana EN; Girard MJA; Ting DSW; Cheng CY; Aung T; Popa-Cherecheanu A; Schmetterer L; Wong D
JAMA Ophthalmol; 2022 Oct; 140(10):974-981. PubMed ID: 36048435
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.
Xiong Z; Cheng Z; Lin X; Xu C; Liu X; Wang D; Luo X; Zhang Y; Jiang H; Qiao N; Zheng M
Sci China Life Sci; 2022 Mar; 65(3):529-539. PubMed ID: 34319533
[TBL] [Abstract][Full Text] [Related]
19. Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging.
Yan R; Qu L; Wei Q; Huang SC; Shen L; Rubin DL; Xing L; Zhou Y
IEEE Trans Med Imaging; 2023 Jul; 42(7):1932-1943. PubMed ID: 37018314
[TBL] [Abstract][Full Text] [Related]
20. MABAL: a Novel Deep-Learning Architecture for Machine-Assisted Bone Age Labeling.
Mutasa S; Chang PD; Ruzal-Shapiro C; Ayyala R
J Digit Imaging; 2018 Aug; 31(4):513-519. PubMed ID: 29404850
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
