147 related articles for article (PubMed ID: 34456649)
1. Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci.
Wang D; Gong Z; Zhang Y; Wang S
Contrast Media Mol Imaging; 2021; 2021():2033806. PubMed ID: 34456649
[TBL] [Abstract][Full Text] [Related]
2. Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images.
Qi Y; Li J; Chen H; Guo Y; Yin Y; Gong G; Wang L
Int J Comput Assist Radiol Surg; 2021 Jun; 16(6):871-882. PubMed ID: 33782844
[TBL] [Abstract][Full Text] [Related]
3. Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network.
Li Q; Xu Y; Chen Z; Liu D; Feng ST; Law M; Ye Y; Huang B
Biomed Res Int; 2018; 2018():9128527. PubMed ID: 30417017
[TBL] [Abstract][Full Text] [Related]
4. Convolutional neural network in nasopharyngeal carcinoma: how good is automatic delineation for primary tumor on a non-contrast-enhanced fat-suppressed T2-weighted MRI?
Wong LM; Ai QYH; Mo FKF; Poon DMC; King AD
Jpn J Radiol; 2021 Jun; 39(6):571-579. PubMed ID: 33544302
[TBL] [Abstract][Full Text] [Related]
5. Magnetic Resonance Imaging Features on Deep Learning Algorithm for the Diagnosis of Nasopharyngeal Carcinoma.
Huang R; Zhou Z; Wang X; Cao X
Contrast Media Mol Imaging; 2022; 2022():3790269. PubMed ID: 35677026
[TBL] [Abstract][Full Text] [Related]
6. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images.
Ke L; Deng Y; Xia W; Qiang M; Chen X; Liu K; Jing B; He C; Xie C; Guo X; Lv X; Li C
Oral Oncol; 2020 Nov; 110():104862. PubMed ID: 32615440
[TBL] [Abstract][Full Text] [Related]
7. Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI.
Wong LM; King AD; Ai QYH; Lam WKJ; Poon DMC; Ma BBY; Chan KCA; Mo FKF
Eur Radiol; 2021 Jun; 31(6):3856-3863. PubMed ID: 33241522
[TBL] [Abstract][Full Text] [Related]
8. Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning.
Ma Z; Zhou S; Wu X; Zhang H; Yan W; Sun S; Zhou J
Phys Med Biol; 2019 Jan; 64(2):025005. PubMed ID: 30524024
[TBL] [Abstract][Full Text] [Related]
9. Quantitative Analysis of DCE-MRI and RESOLVE-DWI for Differentiating Nasopharyngeal Carcinoma from Nasopharyngeal Lymphoid Hyperplasia.
Yu JY; Zhang D; Huang XL; Ma J; Yang C; Li XJ; Xiong H; Zhou B; Liao RK; Tang ZY
J Med Syst; 2020 Feb; 44(4):75. PubMed ID: 32103352
[TBL] [Abstract][Full Text] [Related]
10. Deep Learning for Automated Contouring of Primary Tumor Volumes by MRI for Nasopharyngeal Carcinoma.
Lin L; Dou Q; Jin YM; Zhou GQ; Tang YQ; Chen WL; Su BA; Liu F; Tao CJ; Jiang N; Li JY; Tang LL; Xie CM; Huang SM; Ma J; Heng PA; Wee JTS; Chua MLK; Chen H; Sun Y
Radiology; 2019 Jun; 291(3):677-686. PubMed ID: 30912722
[TBL] [Abstract][Full Text] [Related]
11. Identification and Diagnosis of Cerebral Stroke through Deep Convolutional Neural Network-Based Multimodal MRI Images.
Pan Y; Zhang H; Yang J; Guo J; Yang Z; Wang J; Song G
Contrast Media Mol Imaging; 2021; 2021():7598613. PubMed ID: 34381322
[TBL] [Abstract][Full Text] [Related]
12. Investigation of the feasibility of synthetic MRI in the differential diagnosis of non-keratinising nasopharyngeal carcinoma and benign hyperplasia using different contoured methods for delineation of the region of interest.
Meng T; He H; Liu H; Lv X; Huang C; Zhong L; Liu K; Qian L; Ke L; Xie C
Clin Radiol; 2021 Mar; 76(3):238.e9-238.e15. PubMed ID: 33213835
[TBL] [Abstract][Full Text] [Related]
13. Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.
Zhang J; Zhou Q
Contrast Media Mol Imaging; 2022; 2022():6331206. PubMed ID: 35360270
[TBL] [Abstract][Full Text] [Related]
14. Deep-learning-based detection and segmentation of organs at risk in nasopharyngeal carcinoma computed tomographic images for radiotherapy planning.
Liang S; Tang F; Huang X; Yang K; Zhong T; Hu R; Liu S; Yuan X; Zhang Y
Eur Radiol; 2019 Apr; 29(4):1961-1967. PubMed ID: 30302589
[TBL] [Abstract][Full Text] [Related]
15. The Value of Convolutional Neural Network-Based Magnetic Resonance Imaging Image Segmentation Algorithm to Guide Targeted Controlled Release of Doxorubicin Nanopreparation.
Liu H; Gao H; Jia F
Contrast Media Mol Imaging; 2021; 2021():9032017. PubMed ID: 34385899
[TBL] [Abstract][Full Text] [Related]
16. Auto-segmentation of pancreatic tumor in multi-parametric MRI using deep convolutional neural networks.
Liang Y; Schott D; Zhang Y; Wang Z; Nasief H; Paulson E; Hall W; Knechtges P; Erickson B; Li XA
Radiother Oncol; 2020 Apr; 145():193-200. PubMed ID: 32045787
[TBL] [Abstract][Full Text] [Related]
17. Diagnosis and Treatment Effect of Convolutional Neural Network-Based Magnetic Resonance Image Features on Severe Stroke and Mental State.
Han L; Liu L; Hao Y; Zhang L
Contrast Media Mol Imaging; 2021; 2021():8947789. PubMed ID: 34385898
[TBL] [Abstract][Full Text] [Related]
18. Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms.
Woo I; Lee A; Jung SC; Lee H; Kim N; Cho SJ; Kim D; Lee J; Sunwoo L; Kang DW
Korean J Radiol; 2019 Aug; 20(8):1275-1284. PubMed ID: 31339015
[TBL] [Abstract][Full Text] [Related]
19. Magnetic Resonance Imaging Image Feature Analysis Algorithm under Convolutional Neural Network in the Diagnosis and Risk Stratification of Prostate Cancer.
Gao W; Zhang P; Wang H; Tuo P; Li Z
J Healthc Eng; 2021; 2021():1034661. PubMed ID: 34873435
[TBL] [Abstract][Full Text] [Related]
20. Femoral image segmentation based on two-stage convolutional network using 3D-DMFNet and 3D-ResUnet.
Zhang X; Zheng Y; Bai X; Cai L; Wang L; Wu S; Ke Q; Huang J
Comput Methods Programs Biomed; 2022 Nov; 226():107110. PubMed ID: 36167001
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]