375 related articles for article (PubMed ID: 30253801)
1. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies.
Li C; Jing B; Ke L; Li B; Xia W; He C; Qian C; Zhao C; Mai H; Chen M; Cao K; Mo H; Guo L; Chen Q; Tang L; Qiu W; Yu Y; Liang H; Huang X; Liu G; Li W; Wang L; Sun R; Zou X; Guo S; Huang P; Luo D; Qiu F; Wu Y; Hua Y; Liu K; Lv S; Miao J; Xiang Y; Sun Y; Guo X; Lv X
Cancer Commun (Lond); 2018 Sep; 38(1):59. PubMed ID: 30253801
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area.
Deng Y; Li C; Lv X; Xia W; Shen L; Jing B; Li B; Guo X; Sun Y; Xie C; Ke L
Comput Methods Programs Biomed; 2022 Apr; 217():106702. PubMed ID: 35228147
[TBL] [Abstract][Full Text] [Related]
4. The Tumor Target Segmentation of Nasopharyngeal Cancer in CT Images Based on Deep Learning Methods.
Li S; Xiao J; He L; Peng X; Yuan X
Technol Cancer Res Treat; 2019; 18():1533033819884561. PubMed ID: 31736433
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network.
Cho BJ; Bang CS; Park SW; Yang YJ; Seo SI; Lim H; Shin WG; Hong JT; Yoo YT; Hong SH; Choi JH; Lee JJ; Baik GH
Endoscopy; 2019 Dec; 51(12):1121-1129. PubMed ID: 31443108
[TBL] [Abstract][Full Text] [Related]
8. Application of Artificial Intelligence in Radiotherapy of Nasopharyngeal Carcinoma with Magnetic Resonance Imaging.
Zhao W; Zhang D; Mao X
J Healthc Eng; 2022; 2022():4132989. PubMed ID: 35154619
[TBL] [Abstract][Full Text] [Related]
9. Deep learning-based accurate delineation of primary gross tumor volume of nasopharyngeal carcinoma on heterogeneous magnetic resonance imaging: A large-scale and multi-center study.
Luo X; Liao W; He Y; Tang F; Wu M; Shen Y; Huang H; Song T; Li K; Zhang S; Zhang S; Wang G
Radiother Oncol; 2023 Mar; 180():109480. PubMed ID: 36657723
[TBL] [Abstract][Full Text] [Related]
10. [Segmentation of organs at risk in nasopharyngeal cancer for radiotherapy using a self-adaptive Unet network].
Yang X; Li X; Zhang X; Song F; Huang S; Xia Y
Nan Fang Yi Ke Da Xue Xue Bao; 2020 Nov; 40(11):1579-1586. PubMed ID: 33243744
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. 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]
13. Polycystic liver: automatic segmentation using deep learning on CT is faster and as accurate compared to manual segmentation.
Cayot B; Milot L; Nempont O; Vlachomitrou AS; Langlois-Jacques C; Dumortier J; Boillot O; Arnaud K; Barten TRM; Drenth JPH; Valette PJ
Eur Radiol; 2022 Jul; 32(7):4780-4790. PubMed ID: 35142898
[TBL] [Abstract][Full Text] [Related]
14. Development and Validation of a Deep Learning System for Segmentation of Abdominal Muscle and Fat on Computed Tomography.
Park HJ; Shin Y; Park J; Kim H; Lee IS; Seo DW; Huh J; Lee TY; Park T; Lee J; Kim KW
Korean J Radiol; 2020 Jan; 21(1):88-100. PubMed ID: 31920032
[TBL] [Abstract][Full Text] [Related]
15. Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.
Xu J; Wang J; Bian X; Zhu JQ; Tie CW; Liu X; Zhou Z; Ni XG; Qian D
Laryngoscope; 2022 May; 132(5):999-1007. PubMed ID: 34622964
[TBL] [Abstract][Full Text] [Related]
16. Computer-Aided Pathologic Diagnosis of Nasopharyngeal Carcinoma Based on Deep Learning.
Diao S; Hou J; Yu H; Zhao X; Sun Y; Lambo RL; Xie Y; Liu L; Qin W; Luo W
Am J Pathol; 2020 Aug; 190(8):1691-1700. PubMed ID: 32360568
[TBL] [Abstract][Full Text] [Related]
17. Spotting malignancies from gastric endoscopic images using deep learning.
Lee JH; Kim YJ; Kim YW; Park S; Choi YI; Kim YJ; Park DK; Kim KG; Chung JW
Surg Endosc; 2019 Nov; 33(11):3790-3797. PubMed ID: 30719560
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Fully automatic tumor segmentation of breast ultrasound images with deep learning.
Zhang S; Liao M; Wang J; Zhu Y; Zhang Y; Zhang J; Zheng R; Lv L; Zhu D; Chen H; Wang W
J Appl Clin Med Phys; 2023 Jan; 24(1):e13863. PubMed ID: 36495018
[TBL] [Abstract][Full Text] [Related]
20. [Study on the accuracy of automatic segmentation of knee CT images based on deep learning].
Song P; Fan Z; Zhi X; Cao Z; Min S; Liu X; Zhang Y; Kong X; Chai W
Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi; 2022 May; 36(5):534-539. PubMed ID: 35570625
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]