498 related articles for article (PubMed ID: 33793650)
1. Semi-supervised learning for an improved diagnosis of COVID-19 in CT images.
Han CH; Kim M; Kwak JT
PLoS One; 2021; 16(4):e0249450. PubMed ID: 33793650
[TBL] [Abstract][Full Text] [Related]
2. Lung Lesion Localization of COVID-19 From Chest CT Image: A Novel Weakly Supervised Learning Method.
Yang Z; Zhao L; Wu S; Chen CY
IEEE J Biomed Health Inform; 2021 Jun; 25(6):1864-1872. PubMed ID: 33739926
[TBL] [Abstract][Full Text] [Related]
3. Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.
Qi X; Brown LG; Foran DJ; Nosher J; Hacihaliloglu I
Int J Comput Assist Radiol Surg; 2021 Feb; 16(2):197-206. PubMed ID: 33420641
[TBL] [Abstract][Full Text] [Related]
4. Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning.
Sahoo P; Roy I; Ahlawat R; Irtiza S; Khan L
Phys Eng Sci Med; 2022 Mar; 45(1):31-42. PubMed ID: 34780042
[TBL] [Abstract][Full Text] [Related]
5. A semi-supervised learning method of latent features based on convolutional neural networks for CT metal artifact reduction.
Shi Z; Wang N; Kong F; Cao H; Cao Q
Med Phys; 2022 Jun; 49(6):3845-3859. PubMed ID: 35322430
[TBL] [Abstract][Full Text] [Related]
6. A Weakly-Supervised Framework for COVID-19 Classification and Lesion Localization From Chest CT.
Wang X; Deng X; Fu Q; Zhou Q; Feng J; Ma H; Liu W; Zheng C
IEEE Trans Med Imaging; 2020 Aug; 39(8):2615-2625. PubMed ID: 33156775
[TBL] [Abstract][Full Text] [Related]
7. Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images.
Fan DP; Zhou T; Ji GP; Zhou Y; Chen G; Fu H; Shen J; Shao L
IEEE Trans Med Imaging; 2020 Aug; 39(8):2626-2637. PubMed ID: 32730213
[TBL] [Abstract][Full Text] [Related]
8. COVID-19 disease identification network based on weakly supervised feature selection.
Liu J; Feng Q; Miao Y; He W; Shi W; Jiang Z
Math Biosci Eng; 2023 Mar; 20(5):9327-9348. PubMed ID: 37161245
[TBL] [Abstract][Full Text] [Related]
9. Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan.
Yang D; Xu Z; Li W; Myronenko A; Roth HR; Harmon S; Xu S; Turkbey B; Turkbey E; Wang X; Zhu W; Carrafiello G; Patella F; Cariati M; Obinata H; Mori H; Tamura K; An P; Wood BJ; Xu D
Med Image Anal; 2021 May; 70():101992. PubMed ID: 33601166
[TBL] [Abstract][Full Text] [Related]
10. Semi-supervised COVID-19 CT image segmentation using deep generative models.
Zammit J; Fung DLX; Liu Q; Leung CK; Hu P
BMC Bioinformatics; 2022 Aug; 23(Suppl 7):343. PubMed ID: 35974325
[TBL] [Abstract][Full Text] [Related]
11. NIA-Network: Towards improving lung CT infection detection for COVID-19 diagnosis.
Li W; Chen J; Chen P; Yu L; Cui X; Li Y; Cheng F; Ouyang W
Artif Intell Med; 2021 Jul; 117():102082. PubMed ID: 34127245
[TBL] [Abstract][Full Text] [Related]
12. Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.
Alshazly H; Linse C; Barth E; Martinetz T
Sensors (Basel); 2021 Jan; 21(2):. PubMed ID: 33440674
[TBL] [Abstract][Full Text] [Related]
13. Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling.
Li G; Togo R; Ogawa T; Haseyama M
Comput Biol Med; 2023 May; 158():106877. PubMed ID: 37019015
[TBL] [Abstract][Full Text] [Related]
14. RCoNet: Deformable Mutual Information Maximization and High-Order Uncertainty-Aware Learning for Robust COVID-19 Detection.
Dong S; Yang Q; Fu Y; Tian M; Zhuo C
IEEE Trans Neural Netw Learn Syst; 2021 Aug; 32(8):3401-3411. PubMed ID: 34143745
[TBL] [Abstract][Full Text] [Related]
15. Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.
Xie Y; Zhang J; Xia Y
Med Image Anal; 2019 Oct; 57():237-248. PubMed ID: 31352126
[TBL] [Abstract][Full Text] [Related]
16. COVID19XrayNet: A Two-Step Transfer Learning Model for the COVID-19 Detecting Problem Based on a Limited Number of Chest X-Ray Images.
Zhang R; Guo Z; Sun Y; Lu Q; Xu Z; Yao Z; Duan M; Liu S; Ren Y; Huang L; Zhou F
Interdiscip Sci; 2020 Dec; 12(4):555-565. PubMed ID: 32959234
[TBL] [Abstract][Full Text] [Related]
17. An Effective Semi-Supervised Approach for Liver CT Image Segmentation.
Han K; Liu L; Song Y; Liu Y; Qiu C; Tang Y; Teng Q; Liu Z
IEEE J Biomed Health Inform; 2022 Aug; 26(8):3999-4007. PubMed ID: 35420991
[TBL] [Abstract][Full Text] [Related]
18. SSA-Net: Spatial self-attention network for COVID-19 pneumonia infection segmentation with semi-supervised few-shot learning.
Wang X; Yuan Y; Guo D; Huang X; Cui Y; Xia M; Wang Z; Bai C; Chen S
Med Image Anal; 2022 Jul; 79():102459. PubMed ID: 35544999
[TBL] [Abstract][Full Text] [Related]
19. UMLF-COVID: an unsupervised meta-learning model specifically designed to identify X-ray images of COVID-19 patients.
Miao R; Dong X; Xie SL; Liang Y; Lo SL
BMC Med Imaging; 2021 Nov; 21(1):174. PubMed ID: 34809589
[TBL] [Abstract][Full Text] [Related]
20. Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19.
Fung DLX; Liu Q; Zammit J; Leung CK; Hu P
J Transl Med; 2021 Jul; 19(1):318. PubMed ID: 34311742
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]