BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

297 related articles for article (PubMed ID: 31307017)

  • 1. Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.
    Li S; Xu P; Li B; Chen L; Zhou Z; Hao H; Duan Y; Folkert M; Ma J; Huang S; Jiang S; Wang J
    Phys Med Biol; 2019 Sep; 64(17):175012. PubMed ID: 31307017
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Automated Lung Nodule Detection and Classification Using Deep Learning Combined with Multiple Strategies.
    Nasrullah N; Sang J; Alam MS; Mateen M; Cai B; Hu H
    Sensors (Basel); 2019 Aug; 19(17):. PubMed ID: 31466261
    [TBL] [Abstract][Full Text] [Related]  

  • 3. A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.
    Wang H; Zhao T; Li LC; Pan H; Liu W; Gao H; Han F; Wang Y; Qi Y; Liang Z
    J Xray Sci Technol; 2018; 26(2):171-187. PubMed ID: 29036877
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.
    Han G; Liu X; Zheng G; Wang M; Huang S
    Med Biol Eng Comput; 2018 Dec; 56(12):2201-2212. PubMed ID: 29873026
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Agile convolutional neural network for pulmonary nodule classification using CT images.
    Zhao X; Liu L; Qi S; Teng Y; Li J; Qian W
    Int J Comput Assist Radiol Surg; 2018 Apr; 13(4):585-595. PubMed ID: 29473129
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automatic lung nodule detection using a 3D deep convolutional neural network combined with a multi-scale prediction strategy in chest CTs.
    Gu Y; Lu X; Yang L; Zhang B; Yu D; Zhao Y; Gao L; Wu L; Zhou T
    Comput Biol Med; 2018 Dec; 103():220-231. PubMed ID: 30390571
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images.
    Lyu J; Ling SH
    Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():686-689. PubMed ID: 30440489
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Incorporating automatically learned pulmonary nodule attributes into a convolutional neural network to improve accuracy of benign-malignant nodule classification.
    Dai Y; Yan S; Zheng B; Song C
    Phys Med Biol; 2018 Dec; 63(24):245004. PubMed ID: 30524071
    [TBL] [Abstract][Full Text] [Related]  

  • 9. The effects of physics-based data augmentation on the generalizability of deep neural networks: Demonstration on nodule false-positive reduction.
    Omigbodun AO; Noo F; McNitt-Gray M; Hsu W; Hsieh SS
    Med Phys; 2019 Oct; 46(10):4563-4574. PubMed ID: 31396974
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A deep learning framework for automatic detection of arbitrarily shaped fiducial markers in intrafraction fluoroscopic images.
    Mylonas A; Keall PJ; Booth JT; Shieh CC; Eade T; Poulsen PR; Nguyen DT
    Med Phys; 2019 May; 46(5):2286-2297. PubMed ID: 30929254
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.
    Zhao X; Qi S; Zhang B; Ma H; Qian W; Yao Y; Sun J
    J Xray Sci Technol; 2019; 27(4):615-629. PubMed ID: 31227682
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss.
    Tran GS; Nghiem TP; Nguyen VT; Luong CM; Burie JC
    J Healthc Eng; 2019; 2019():5156416. PubMed ID: 30863524
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.
    Eun H; Kim D; Jung C; Kim C
    Comput Methods Programs Biomed; 2018 Oct; 165():215-224. PubMed ID: 30337076
    [TBL] [Abstract][Full Text] [Related]  

  • 14. 3D multi-view convolutional neural networks for lung nodule classification.
    Kang G; Liu K; Hou B; Zhang N
    PLoS One; 2017; 12(11):e0188290. PubMed ID: 29145492
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.
    Zhang C; Sun X; Dang K; Li K; Guo XW; Chang J; Yu ZQ; Huang FY; Wu YS; Liang Z; Liu ZY; Zhang XG; Gao XL; Huang SH; Qin J; Feng WN; Zhou T; Zhang YB; Fang WJ; Zhao MF; Yang XN; Zhou Q; Wu YL; Zhong WZ
    Oncologist; 2019 Sep; 24(9):1159-1165. PubMed ID: 30996009
    [TBL] [Abstract][Full Text] [Related]  

  • 16. A multi-view deep convolutional neural networks for lung nodule segmentation.
    Shuo Wang ; Mu Zhou ; Gevaert O; Zhenchao Tang ; Di Dong ; Zhenyu Liu ; Jie Tian
    Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():1752-1755. PubMed ID: 29060226
    [TBL] [Abstract][Full Text] [Related]  

  • 17. CAD system for lung nodule detection using deep learning with CNN.
    Manickavasagam R; Selvan S; Selvan M
    Med Biol Eng Comput; 2022 Jan; 60(1):221-228. PubMed ID: 34811644
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Deep feature classification of angiomyolipoma without visible fat and renal cell carcinoma in abdominal contrast-enhanced CT images with texture image patches and hand-crafted feature concatenation.
    Lee H; Hong H; Kim J; Jung DC
    Med Phys; 2018 Apr; 45(4):1550-1561. PubMed ID: 29474742
    [TBL] [Abstract][Full Text] [Related]  

  • 19. CNN models discriminating between pulmonary micro-nodules and non-nodules from CT images.
    Monkam P; Qi S; Xu M; Han F; Zhao X; Qian W
    Biomed Eng Online; 2018 Jul; 17(1):96. PubMed ID: 30012167
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Pulmonary nodule segmentation with CT sample synthesis using adversarial networks.
    Qin Y; Zheng H; Huang X; Yang J; Zhu YM
    Med Phys; 2019 Mar; 46(3):1218-1229. PubMed ID: 30575046
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 15.