292 related articles for article (PubMed ID: 31454710)
1. An investigation of CNN models for differentiating malignant from benign lesions using small pathologically proven datasets.
Zhang S; Han F; Liang Z; Tan J; Cao W; Gao Y; Pomeroy M; Ng K; Hou W
Comput Med Imaging Graph; 2019 Oct; 77():101645. PubMed ID: 31454710
[TBL] [Abstract][Full Text] [Related]
2. A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.
Huang W; Xue Y; Wu Y
PLoS One; 2019; 14(7):e0219369. PubMed ID: 31299053
[TBL] [Abstract][Full Text] [Related]
3. Predicting Unnecessary Nodule Biopsies from a Small, Unbalanced, and Pathologically Proven Dataset by Transfer Learning.
Han F; Yan L; Chen J; Teng Y; Chen S; Qi S; Qian W; Yang J; Moore W; Zhang S; Liang Z
J Digit Imaging; 2020 Jun; 33(3):685-696. PubMed ID: 32144499
[TBL] [Abstract][Full Text] [Related]
4. MM-GLCM-CNN: A multi-scale and multi-level based GLCM-CNN for polyp classification.
Zhang S; Wu J; Shi E; Yu S; Gao Y; Li LC; Kuo LR; Pomeroy MJ; Liang ZJ
Comput Med Imaging Graph; 2023 Sep; 108():102257. PubMed ID: 37301171
[TBL] [Abstract][Full Text] [Related]
5. 3D-GLCM CNN: A 3-Dimensional Gray-Level Co-Occurrence Matrix-Based CNN Model for Polyp Classification via CT Colonography.
Tan J; Gao Y; Liang Z; Cao W; Pomeroy MJ; Huo Y; Li L; Barish MA; Abbasi AF; Pickhardt PJ
IEEE Trans Med Imaging; 2020 Jun; 39(6):2013-2024. PubMed ID: 31899419
[TBL] [Abstract][Full Text] [Related]
6. Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.
Ohno Y; Aoyagi K; Yaguchi A; Seki S; Ueno Y; Kishida Y; Takenaka D; Yoshikawa T
Radiology; 2020 Aug; 296(2):432-443. PubMed ID: 32452736
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy.
Urban G; Tripathi P; Alkayali T; Mittal M; Jalali F; Karnes W; Baldi P
Gastroenterology; 2018 Oct; 155(4):1069-1078.e8. PubMed ID: 29928897
[TBL] [Abstract][Full Text] [Related]
9. 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]
10. 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]
11. 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]
12. Deep learning assisted detection of glaucomatous optic neuropathy and potential designs for a generalizable model.
Ko YC; Wey SY; Chen WT; Chang YF; Chen MJ; Chiou SH; Liu CJ; Lee CY
PLoS One; 2020; 15(5):e0233079. PubMed ID: 32407355
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. Segmentation of lung parenchyma in CT images using CNN trained with the clustering algorithm generated dataset.
Xu M; Qi S; Yue Y; Teng Y; Xu L; Yao Y; Qian W
Biomed Eng Online; 2019 Jan; 18(1):2. PubMed ID: 30602393
[TBL] [Abstract][Full Text] [Related]
15. A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.
Zhao HB; Liu C; Ye J; Chang LF; Xu Q; Shi BW; Liu LL; Yin YL; Shi BB
Endokrynol Pol; 2021; 72(3):217-225. PubMed ID: 33619712
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. 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]
18. 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]
19. Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network.
Chen CH; Lee YW; Huang YS; Lan WR; Chang RF; Tu CY; Chen CY; Liao WC
Comput Methods Programs Biomed; 2019 Aug; 177():175-182. PubMed ID: 31319946
[TBL] [Abstract][Full Text] [Related]
20. Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation.
Wang S; Zhou M; Liu Z; Liu Z; Gu D; Zang Y; Dong D; Gevaert O; Tian J
Med Image Anal; 2017 Aug; 40():172-183. PubMed ID: 28688283
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]