105 related articles for article (PubMed ID: 36541557)
1. A multi-perspective information aggregation network for automated
Liang S; Dong X; Yang K; Chu Z; Tang F; Ye F; Chen B; Guan J; Zhang Y
Phys Med Biol; 2022 Dec; 67(24):. PubMed ID: 36541557
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. 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]
4. Virtual Contrast-Enhanced Magnetic Resonance Images Synthesis for Patients With Nasopharyngeal Carcinoma Using Multimodality-Guided Synergistic Neural Network.
Li W; Xiao H; Li T; Ren G; Lam S; Teng X; Liu C; Zhang J; Kar-Ho Lee F; Au KH; Ho-Fun Lee V; Chang ATY; Cai J
Int J Radiat Oncol Biol Phys; 2022 Mar; 112(4):1033-1044. PubMed ID: 34774997
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Automatic T Staging Using Weakly Supervised Deep Learning for Nasopharyngeal Carcinoma on MR Images.
Yang Q; Guo Y; Ou X; Wang J; Hu C
J Magn Reson Imaging; 2020 Oct; 52(4):1074-1082. PubMed ID: 32583578
[TBL] [Abstract][Full Text] [Related]
7. Pseudo-CT generation from multi-parametric MRI using a novel multi-channel multi-path conditional generative adversarial network for nasopharyngeal carcinoma patients.
Tie X; Lam SK; Zhang Y; Lee KH; Au KH; Cai J
Med Phys; 2020 Apr; 47(4):1750-1762. PubMed ID: 32012292
[TBL] [Abstract][Full Text] [Related]
8. DCNet: Densely Connected Deep Convolutional Encoder-Decoder Network for Nasopharyngeal Carcinoma Segmentation.
Li Y; Han G; Liu X
Sensors (Basel); 2021 Nov; 21(23):. PubMed ID: 34883878
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. Postoperative glioma segmentation in CT image using deep feature fusion model guided by multi-sequence MRIs.
Tang F; Liang S; Zhong T; Huang X; Deng X; Zhang Y; Zhou L
Eur Radiol; 2020 Feb; 30(2):823-832. PubMed ID: 31650265
[TBL] [Abstract][Full Text] [Related]
12. Improved accuracy of auto-segmentation of organs at risk in radiotherapy planning for nasopharyngeal carcinoma based on fully convolutional neural network deep learning.
Peng Y; Liu Y; Shen G; Chen Z; Chen M; Miao J; Zhao C; Deng J; Qi Z; Deng X
Oral Oncol; 2023 Jan; 136():106261. PubMed ID: 36446186
[TBL] [Abstract][Full Text] [Related]
13. Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black-blood vessel wall MRI.
Wu J; Xin J; Yang X; Sun J; Xu D; Zheng N; Yuan C
Med Phys; 2019 Dec; 46(12):5544-5561. PubMed ID: 31356693
[TBL] [Abstract][Full Text] [Related]
14. Radiomics on multi-modalities MR sequences can subtype patients with non-metastatic nasopharyngeal carcinoma (NPC) into distinct survival subgroups.
Zhuo EH; Zhang WJ; Li HJ; Zhang GY; Jing BZ; Zhou J; Cui CY; Chen MY; Sun Y; Liu LZ; Cai HM
Eur Radiol; 2019 Oct; 29(10):5590-5599. PubMed ID: 30874880
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Automated segmentation of the left ventricle from MR cine imaging based on deep learning architecture.
Qin W; Wu Y; Li S; Chen Y; Yang Y; Liu X; Zheng H; Liang D; Hu Z
Biomed Phys Eng Express; 2020 Feb; 6(2):025009. PubMed ID: 33438635
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. SeqSeg: A sequential method to achieve nasopharyngeal carcinoma segmentation free from background dominance.
Tao G; Li H; Huang J; Han C; Chen J; Ruan G; Huang W; Hu Y; Dan T; Zhang B; He S; Liu L; Cai H
Med Image Anal; 2022 May; 78():102381. PubMed ID: 35231849
[TBL] [Abstract][Full Text] [Related]
19. DLNLF-net: Denoised local and non-local deep features fusion network for malignancy characterization of hepatocellular carcinoma.
Huang H; Xie Y; Wang G; Zhang L; Zhou W
Comput Methods Programs Biomed; 2022 Dec; 227():107201. PubMed ID: 36335751
[TBL] [Abstract][Full Text] [Related]
20. Medical image diagnosis of prostate tumor based on PSP-Net+VGG16 deep learning network.
Ye LY; Miao XY; Cai WS; Xu WJ
Comput Methods Programs Biomed; 2022 Jun; 221():106770. PubMed ID: 35640389
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
