364 related articles for article (PubMed ID: 30415720)
1. Cascaded classifiers and stacking methods for classification of pulmonary nodule characteristics.
Kaya A
Comput Methods Programs Biomed; 2018 Nov; 166():77-89. PubMed ID: 30415720
[TBL] [Abstract][Full Text] [Related]
2. A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics.
Kaya A; Can AB
J Biomed Inform; 2015 Aug; 56():69-79. PubMed ID: 26008877
[TBL] [Abstract][Full Text] [Related]
3. A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.
Messay T; Hardie RC; Rogers SK
Med Image Anal; 2010 Jun; 14(3):390-406. PubMed ID: 20346728
[TBL] [Abstract][Full Text] [Related]
4. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the Lung Image Database Consortium and Image Database Resource Initiative dataset.
Messay T; Hardie RC; Tuinstra TR
Med Image Anal; 2015 May; 22(1):48-62. PubMed ID: 25791434
[TBL] [Abstract][Full Text] [Related]
5. Automated lung nodule classification following automated nodule detection on CT: a serial approach.
Armato SG; Altman MB; Wilkie J; Sone S; Li F; Doi K; Roy AS
Med Phys; 2003 Jun; 30(6):1188-97. PubMed ID: 12852543
[TBL] [Abstract][Full Text] [Related]
6. Measuring Interobserver Disagreement in Rating Diagnostic Characteristics of Pulmonary Nodule Using the Lung Imaging Database Consortium and Image Database Resource Initiative.
Lin H; Huang C; Wang W; Luo J; Yang X; Liu Y
Acad Radiol; 2017 Apr; 24(4):401-410. PubMed ID: 28169141
[TBL] [Abstract][Full Text] [Related]
7. Pulmonary Nodule Classification with Deep Convolutional Neural Networks on Computed Tomography Images.
Li W; Cao P; Zhao D; Wang J
Comput Math Methods Med; 2016; 2016():6215085. PubMed ID: 28070212
[TBL] [Abstract][Full Text] [Related]
8. Detection of pulmonary ground-glass opacity based on deep learning computer artificial intelligence.
Ye W; Gu W; Guo X; Yi P; Meng Y; Han F; Yu L; Chen Y; Zhang G; Wang X
Biomed Eng Online; 2019 Jan; 18(1):6. PubMed ID: 30670024
[TBL] [Abstract][Full Text] [Related]
9. Computer-aided lung nodule recognition by SVM classifier based on combination of random undersampling and SMOTE.
Sui Y; Wei Y; Zhao D
Comput Math Methods Med; 2015; 2015():368674. PubMed ID: 25977704
[TBL] [Abstract][Full Text] [Related]
10. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative.
Wang W; Luo J; Yang X; Lin H
Acad Radiol; 2015 Apr; 22(4):488-95. PubMed ID: 25601306
[TBL] [Abstract][Full Text] [Related]
11. Toward Understanding the Size Dependence of Shape Features for Predicting Spiculation in Lung Nodules for Computer-Aided Diagnosis.
Niehaus R; Raicu DS; Furst J; Armato S
J Digit Imaging; 2015 Dec; 28(6):704-17. PubMed ID: 25708891
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Computer-aided diagnosis of pulmonary nodules using a two-step approach for feature selection and classifier ensemble construction.
Lee MC; Boroczky L; Sungur-Stasik K; Cann AD; Borczuk AC; Kawut SM; Powell CA
Artif Intell Med; 2010 Sep; 50(1):43-53. PubMed ID: 20570118
[TBL] [Abstract][Full Text] [Related]
15. Multi-scale Convolutional Neural Networks for Lung Nodule Classification.
Shen W; Zhou M; Yang F; Yang C; Tian J
Inf Process Med Imaging; 2015; 24():588-99. PubMed ID: 26221705
[TBL] [Abstract][Full Text] [Related]
16. An analysis of early studies released by the Lung Imaging Database Consortium (LIDC).
Ross JC; Miller JV; Turner WD; Kelliher TP
Acad Radiol; 2007 Nov; 14(11):1382-8. PubMed ID: 17964461
[TBL] [Abstract][Full Text] [Related]
17. Computer aided characterization of the solitary pulmonary nodule using volumetric and contrast enhancement features.
Shah SK; McNitt-Gray MF; Rogers SR; Goldin JG; Suh RD; Sayre JW; Petkovska I; Kim HJ; Aberle DR
Acad Radiol; 2005 Oct; 12(10):1310-9. PubMed ID: 16179208
[TBL] [Abstract][Full Text] [Related]
18. A novel computer-aided lung nodule detection system for CT images.
Tan M; Deklerck R; Jansen B; Bister M; Cornelis J
Med Phys; 2011 Oct; 38(10):5630-45. PubMed ID: 21992380
[TBL] [Abstract][Full Text] [Related]
19. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
Way TW; Hadjiiski LM; Sahiner B; Chan HP; Cascade PN; Kazerooni EA; Bogot N; Zhou C
Med Phys; 2006 Jul; 33(7):2323-37. PubMed ID: 16898434
[TBL] [Abstract][Full Text] [Related]
20. Robust explanation supervision for false positive reduction in pulmonary nodule detection.
Zhao Q; Chang CW; Yang X; Zhao L
Med Phys; 2024 Mar; 51(3):1687-1701. PubMed ID: 38224306
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
