495 related articles for article (PubMed ID: 30279243)
1. 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas.
Zhao W; Yang J; Sun Y; Li C; Wu W; Jin L; Yang Z; Ni B; Gao P; Wang P; Hua Y; Li M
Cancer Res; 2018 Dec; 78(24):6881-6889. PubMed ID: 30279243
[TBL] [Abstract][Full Text] [Related]
2. Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.
Wang J; Chen X; Lu H; Zhang L; Pan J; Bao Y; Su J; Qian D
Med Phys; 2020 Apr; 47(4):1738-1749. PubMed ID: 32020649
[TBL] [Abstract][Full Text] [Related]
3. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Gong J; Liu J; Hao W; Nie S; Zheng B; Wang S; Peng W
Eur Radiol; 2020 Apr; 30(4):1847-1855. PubMed ID: 31811427
[TBL] [Abstract][Full Text] [Related]
4. Determining the invasiveness of ground-glass nodules using a 3D multi-task network.
Yu Y; Wang N; Huang N; Liu X; Zheng Y; Fu Y; Li X; Wu H; Xu J; Cheng J
Eur Radiol; 2021 Sep; 31(9):7162-7171. PubMed ID: 33665717
[TBL] [Abstract][Full Text] [Related]
5. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
[TBL] [Abstract][Full Text] [Related]
6. 3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.
Wang D; Zhang T; Li M; Bueno R; Jayender J
Comput Med Imaging Graph; 2021 Mar; 88():101814. PubMed ID: 33486368
[TBL] [Abstract][Full Text] [Related]
7. Lung-PNet: An Automated Deep Learning Model for the Diagnosis of Invasive Adenocarcinoma in Pure Ground-Glass Nodules on Chest CT.
Qi K; Wang K; Wang X; Zhang YD; Lin G; Zhang X; Liu H; Huang W; Wu J; Zhao K; Liu J; Li J; Zhang X
AJR Am J Roentgenol; 2024 Jan; 222(1):e2329674. PubMed ID: 37493322
[No Abstract] [Full Text] [Related]
8. Computer-aided diagnosis of ground glass pulmonary nodule by fusing deep learning and radiomics features.
Hu X; Gong J; Zhou W; Li H; Wang S; Wei M; Peng W; Gu Y
Phys Med Biol; 2021 Mar; 66(6):065015. PubMed ID: 33596552
[TBL] [Abstract][Full Text] [Related]
9. Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.
Ashraf SF; Yin K; Meng CX; Wang Q; Wang Q; Pu J; Dhupar R
J Thorac Cardiovasc Surg; 2022 Apr; 163(4):1496-1505.e10. PubMed ID: 33726909
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. Imaging features of TSCT predict the classification of pulmonary preinvasive lesion, minimally and invasive adenocarcinoma presented as ground glass nodules.
Liu Y; Sun H; Zhou F; Su C; Gao G; Ren S; Zhou C; Zhang Z; Shi J
Lung Cancer; 2017 Jun; 108():192-197. PubMed ID: 28625634
[TBL] [Abstract][Full Text] [Related]
12. Computer-aided diagnosis of ground-glass opacity pulmonary nodules using radiomic features analysis.
Gong J; Liu J; Hao W; Nie S; Wang S; Peng W
Phys Med Biol; 2019 Jul; 64(13):135015. PubMed ID: 31167172
[TBL] [Abstract][Full Text] [Related]
13. Toward automatic prediction of EGFR mutation status in pulmonary adenocarcinoma with 3D deep learning.
Zhao W; Yang J; Ni B; Bi D; Sun Y; Xu M; Zhu X; Li C; Jin L; Gao P; Wang P; Hua Y; Li M
Cancer Med; 2019 Jul; 8(7):3532-3543. PubMed ID: 31074592
[TBL] [Abstract][Full Text] [Related]
14. Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images.
Masood A; Sheng B; Li P; Hou X; Wei X; Qin J; Feng D
J Biomed Inform; 2018 Mar; 79():117-128. PubMed ID: 29366586
[TBL] [Abstract][Full Text] [Related]
15. High-resolution CT image analysis based on 3D convolutional neural network can enhance the classification performance of radiologists in classifying pulmonary non-solid nodules.
Zhang T; Wang Y; Sun Y; Yuan M; Zhong Y; Li H; Yu T; Wang J
Eur J Radiol; 2021 Aug; 141():109810. PubMed ID: 34102564
[TBL] [Abstract][Full Text] [Related]
16. Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.
Park S; Park G; Lee SM; Kim W; Park H; Jung K; Seo JB
Eur Radiol; 2021 Aug; 31(8):6239-6247. PubMed ID: 33555355
[TBL] [Abstract][Full Text] [Related]
17. Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature.
Le V; Yang D; Zhu Y; Zheng B; Bai C; Shi H; Hu J; Zhai C; Lu S
Comput Methods Programs Biomed; 2018 Jul; 160():141-151. PubMed ID: 29728241
[TBL] [Abstract][Full Text] [Related]
18. Solitary solid pulmonary nodules: a CT-based deep learning nomogram helps differentiate tuberculosis granulomas from lung adenocarcinomas.
Feng B; Chen X; Chen Y; Lu S; Liu K; Li K; Liu Z; Hao Y; Li Z; Zhu Z; Yao N; Liang G; Zhang J; Long W; Liu X
Eur Radiol; 2020 Dec; 30(12):6497-6507. PubMed ID: 32594210
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification.
Ren Y; Tsai MY; Chen L; Wang J; Li S; Liu Y; Jia X; Shen C
Int J Comput Assist Radiol Surg; 2020 Feb; 15(2):287-295. PubMed ID: 31768885
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]