145 related articles for article (PubMed ID: 32505895)
1. Identification of benign and malignant pulmonary nodules on chest CT using improved 3D U-Net deep learning framework.
Yang K; Liu J; Tang W; Zhang H; Zhang R; Gu J; Zhu R; Xiong J; Ru X; Wu J
Eur J Radiol; 2020 Aug; 129():109013. PubMed ID: 32505895
[TBL] [Abstract][Full Text] [Related]
2. Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest.
Wang YW; Wang JW; Yang SX; Qi LL; Lin HL; Zhou Z; Yu YZ
Eur Radiol; 2021 Nov; 31(11):8160-8167. PubMed ID: 33956178
[TBL] [Abstract][Full Text] [Related]
3. Deep Learning for the Classification of Small (≤2 cm) Pulmonary Nodules on CT Imaging: A Preliminary Study.
Chae KJ; Jin GY; Ko SB; Wang Y; Zhang H; Choi EJ; Choi H
Acad Radiol; 2020 Apr; 27(4):e55-e63. PubMed ID: 31780395
[TBL] [Abstract][Full Text] [Related]
4. Hybrid U-Net-based deep learning model for volume segmentation of lung nodules in CT images.
Wang Y; Zhou C; Chan HP; Hadjiiski LM; Chughtai A; Kazerooni EA
Med Phys; 2022 Nov; 49(11):7287-7302. PubMed ID: 35717560
[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. Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy.
Li F; Aoyama M; Shiraishi J; Abe H; Li Q; Suzuki K; Engelmann R; Sone S; Macmahon H; Doi K
AJR Am J Roentgenol; 2004 Nov; 183(5):1209-15. PubMed ID: 15505279
[TBL] [Abstract][Full Text] [Related]
7. 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]
8. Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice.
Liang CH; Liu YC; Wu MT; Garcia-Castro F; Alberich-Bayarri A; Wu FZ
Clin Radiol; 2020 Jan; 75(1):38-45. PubMed ID: 31521323
[TBL] [Abstract][Full Text] [Related]
9. Development and validation of a clinically applicable deep learning strategy (HONORS) for pulmonary nodule classification at CT: A retrospective multicentre study.
Lv W; Wang Y; Zhou C; Yuan M; Pang M; Fang X; Zhang Q; Huang C; Li X; Zhou Z; Yu Y; Wang Y; Lu M; Xu Q; Li X; Lin H; Lu X; Xu Q; Sun J; Tang Y; Yan F; Zhang B; Cheng Z; Zhang L; Lu G
Lung Cancer; 2021 May; 155():78-86. PubMed ID: 33761380
[TBL] [Abstract][Full Text] [Related]
10. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography.
Kozuka T; Matsukubo Y; Kadoba T; Oda T; Suzuki A; Hyodo T; Im S; Kaida H; Yagyu Y; Tsurusaki M; Matsuki M; Ishii K
Jpn J Radiol; 2020 Nov; 38(11):1052-1061. PubMed ID: 32592003
[TBL] [Abstract][Full Text] [Related]
11. 3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT.
Zheng B; Yang D; Zhu Y; Liu Y; Hu J; Bai C
Med Phys; 2021 Dec; 48(12):7826-7836. PubMed ID: 34655238
[TBL] [Abstract][Full Text] [Related]
12. JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function.
Lo SB; Freedman MT; Gillis LB; White CS; Mun SK
AJR Am J Roentgenol; 2018 Mar; 210(3):480-488. PubMed ID: 29336601
[TBL] [Abstract][Full Text] [Related]
13. Malignancy risk estimation of pulmonary nodules in screening CTs: Comparison between a computer model and human observers.
van Riel SJ; Ciompi F; Winkler Wille MM; Dirksen A; Lam S; Scholten ET; Rossi SE; Sverzellati N; Naqibullah M; Wittenberg R; Hovinga-de Boer MC; Snoeren M; Peters-Bax L; Mets O; Brink M; Prokop M; Schaefer-Prokop C; van Ginneken B
PLoS One; 2017; 12(11):e0185032. PubMed ID: 29121063
[TBL] [Abstract][Full Text] [Related]
14. Design of lung nodules segmentation and recognition algorithm based on deep learning.
Yu H; Li J; Zhang L; Cao Y; Yu X; Sun J
BMC Bioinformatics; 2021 Nov; 22(Suppl 5):314. PubMed ID: 34749636
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.
Xu Y; Wang S; Sun X; Yang Y; Fan J; Jin W; Li Y; Su F; Zhang W; Cui Q; Hu Y; Wang S; Zhang J; Chen C
Interdiscip Sci; 2022 Mar; 14(1):130-140. PubMed ID: 34727340
[TBL] [Abstract][Full Text] [Related]
17. Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT.
Xie Y; Xia Y; Zhang J; Song Y; Feng D; Fulham M; Cai W
IEEE Trans Med Imaging; 2019 Apr; 38(4):991-1004. PubMed ID: 30334786
[TBL] [Abstract][Full Text] [Related]
18. Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs.
Yoo H; Kim KH; Singh R; Digumarthy SR; Kalra MK
JAMA Netw Open; 2020 Sep; 3(9):e2017135. PubMed ID: 32970157
[TBL] [Abstract][Full Text] [Related]
19. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram.
Liu A; Wang Z; Yang Y; Wang J; Dai X; Wang L; Lu Y; Xue F
Cancer Commun (Lond); 2020 Jan; 40(1):16-24. PubMed ID: 32125097
[TBL] [Abstract][Full Text] [Related]
20. Deep Learning for Malignancy Risk Estimation of Pulmonary Nodules Detected at Low-Dose Screening CT.
Venkadesh KV; Setio AAA; Schreuder A; Scholten ET; Chung K; W Wille MM; Saghir Z; van Ginneken B; Prokop M; Jacobs C
Radiology; 2021 Aug; 300(2):438-447. PubMed ID: 34003056
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
