254 related articles for article (PubMed ID: 30719445)
1. Automated Pulmonary Nodule Classification in Computed Tomography Images Using a Deep Convolutional Neural Network Trained by Generative Adversarial Networks.
Onishi Y; Teramoto A; Tsujimoto M; Tsukamoto T; Saito K; Toyama H; Imaizumi K; Fujita H
Biomed Res Int; 2019; 2019():6051939. PubMed ID: 30719445
[TBL] [Abstract][Full Text] [Related]
2. Multiplanar analysis for pulmonary nodule classification in CT images using deep convolutional neural network and generative adversarial networks.
Onishi Y; Teramoto A; Tsujimoto M; Tsukamoto T; Saito K; Toyama H; Imaizumi K; Fujita H
Int J Comput Assist Radiol Surg; 2020 Jan; 15(1):173-178. PubMed ID: 31732864
[TBL] [Abstract][Full Text] [Related]
3. Investigation of pulmonary nodule classification using multi-scale residual network enhanced with 3DGAN-synthesized volumes.
Onishi Y; Teramoto A; Tsujimoto M; Tsukamoto T; Saito K; Toyama H; Imaizumi K; Fujita H
Radiol Phys Technol; 2020 Jun; 13(2):160-169. PubMed ID: 32358643
[TBL] [Abstract][Full Text] [Related]
4. Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network.
Nishio M; Muramatsu C; Noguchi S; Nakai H; Fujimoto K; Sakamoto R; Fujita H
Comput Biol Med; 2020 Nov; 126():104032. PubMed ID: 33045649
[TBL] [Abstract][Full Text] [Related]
5. Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method.
Jung H; Kim B; Lee I; Lee J; Kang J
BMC Med Imaging; 2018 Dec; 18(1):48. PubMed ID: 30509191
[TBL] [Abstract][Full Text] [Related]
6. Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.
Sim Y; Chung MJ; Kotter E; Yune S; Kim M; Do S; Han K; Kim H; Yang S; Lee DJ; Choi BW
Radiology; 2020 Jan; 294(1):199-209. PubMed ID: 31714194
[TBL] [Abstract][Full Text] [Related]
7. An improved 3-D attention CNN with hybrid loss and feature fusion for pulmonary nodule classification.
Huang YS; Wang TC; Huang SZ; Zhang J; Chen HM; Chang YC; Chang RF
Comput Methods Programs Biomed; 2023 Feb; 229():107278. PubMed ID: 36463674
[TBL] [Abstract][Full Text] [Related]
8. Computer-aided diagnostic scheme for distinction between benign and malignant nodules in thoracic low-dose CT by use of massive training artificial neural network.
Suzuki K; Li F; Sone S; Doi K
IEEE Trans Med Imaging; 2005 Sep; 24(9):1138-50. PubMed ID: 16156352
[TBL] [Abstract][Full Text] [Related]
9. Semi-supervised adversarial model for benign-malignant lung nodule classification on chest CT.
Xie Y; Zhang J; Xia Y
Med Image Anal; 2019 Oct; 57():237-248. PubMed ID: 31352126
[TBL] [Abstract][Full Text] [Related]
10. Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction.
Lv E; Liu W; Wen P; Kang X
J Healthc Eng; 2021; 2021():8769652. PubMed ID: 34745513
[TBL] [Abstract][Full Text] [Related]
11. Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.
Zhang S; Sun F; Wang N; Zhang C; Yu Q; Zhang M; Babyn P; Zhong H
J Digit Imaging; 2019 Dec; 32(6):995-1007. PubMed ID: 31044393
[TBL] [Abstract][Full Text] [Related]
12. Neural network-based computer-aided diagnosis in distinguishing malignant from benign solitary pulmonary nodules by computed tomography.
Chen H; Wang XH; Ma DQ; Ma BR
Chin Med J (Engl); 2007 Jul; 120(14):1211-5. PubMed ID: 17697569
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT?
Suzuki K; Doi K
Acad Radiol; 2005 Oct; 12(10):1333-41. PubMed ID: 16179210
[TBL] [Abstract][Full Text] [Related]
15. Improved lung nodule diagnosis accuracy using lung CT images with uncertain class.
Wang Z; Xin J; Sun P; Lin Z; Yao Y; Gao X
Comput Methods Programs Biomed; 2018 Aug; 162():197-209. PubMed ID: 29903487
[TBL] [Abstract][Full Text] [Related]
16. Classification of benign and malignant lung nodules from CT images based on hybrid features.
Zhang G; Yang Z; Gong L; Jiang S; Wang L
Phys Med Biol; 2019 Jun; 64(12):125011. PubMed ID: 31141794
[TBL] [Abstract][Full Text] [Related]
17. Deep learning approach to classification of lung cytological images: Two-step training using actual and synthesized images by progressive growing of generative adversarial networks.
Teramoto A; Tsukamoto T; Yamada A; Kiriyama Y; Imaizumi K; Saito K; Fujita H
PLoS One; 2020; 15(3):e0229951. PubMed ID: 32134949
[TBL] [Abstract][Full Text] [Related]
18. Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography.
Suzuki K; Armato SG; Li F; Sone S; Doi K
Med Phys; 2003 Jul; 30(7):1602-17. PubMed ID: 12906178
[TBL] [Abstract][Full Text] [Related]
19. Automatic Pulmonary Nodule Detection in CT Scans Using Convolutional Neural Networks Based on Maximum Intensity Projection.
Zheng S; Guo J; Cui X; Veldhuis RNJ; Oudkerk M; van Ooijen PMA
IEEE Trans Med Imaging; 2020 Mar; 39(3):797-805. PubMed ID: 31425026
[TBL] [Abstract][Full Text] [Related]
20. Res-trans networks for lung nodule classification.
Liu D; Liu F; Tie Y; Qi L; Wang F
Int J Comput Assist Radiol Surg; 2022 Jun; 17(6):1059-1068. PubMed ID: 35290646
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
