340 related articles for article (PubMed ID: 18206615)
1. Computerized detection of lung nodules in thin-section CT images by use of selective enhancement filters and an automated rule-based classifier.
Li Q; Li F; Doi K
Acad Radiol; 2008 Feb; 15(2):165-75. PubMed ID: 18206615
[TBL] [Abstract][Full Text] [Related]
2. Benefit of computer-aided detection analysis for the detection of subsolid and solid lung nodules on thin- and thick-section CT.
Godoy MC; Kim TJ; White CS; Bogoni L; de Groot P; Florin C; Obuchowski N; Babb JS; Salganicoff M; Naidich DP; Anand V; Park S; Vlahos I; Ko JP
AJR Am J Roentgenol; 2013 Jan; 200(1):74-83. PubMed ID: 23255744
[TBL] [Abstract][Full Text] [Related]
3. Computerized scheme for automated detection of lung nodules in low-dose computed tomography images for lung cancer screening.
Arimura H; Katsuragawa S; Suzuki K; Li F; Shiraishi J; Sone S; Doi K
Acad Radiol; 2004 Jun; 11(6):617-29. PubMed ID: 15172364
[TBL] [Abstract][Full Text] [Related]
4. A supervised 'lesion-enhancement' filter by use of a massive-training artificial neural network (MTANN) in computer-aided diagnosis (CAD).
Suzuki K
Phys Med Biol; 2009 Sep; 54(18):S31-45. PubMed ID: 19687563
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases.
Yanagawa M; Honda O; Yoshida S; Ono Y; Inoue A; Daimon T; Sumikawa H; Mihara N; Johkoh T; Tomiyama N; Nakamura H
Acad Radiol; 2009 Aug; 16(8):924-33. PubMed ID: 19394873
[TBL] [Abstract][Full Text] [Related]
7. A computerized scheme for lung nodule detection in multiprojection chest radiography.
Guo W; Li Q; Boyce SJ; McAdams HP; Shiraishi J; Doi K; Samei E
Med Phys; 2012 Apr; 39(4):2001-12. PubMed ID: 22482621
[TBL] [Abstract][Full Text] [Related]
8. High performance lung nodule detection schemes in CT using local and global information.
Guo W; Li Q
Med Phys; 2012 Aug; 39(8):5157-68. PubMed ID: 22894441
[TBL] [Abstract][Full Text] [Related]
9. Effect of segmentation algorithms on the performance of computerized detection of lung nodules in CT.
Guo W; Li Q
Med Phys; 2014 Sep; 41(9):091906. PubMed ID: 25186393
[TBL] [Abstract][Full Text] [Related]
10. Evaluation of automated lung nodule detection on low-dose computed tomography scans from a lung cancer screening program(1).
Armato SG; Roy AS; Macmahon H; Li F; Doi K; Sone S; Altman MB
Acad Radiol; 2005 Mar; 12(3):337-46. PubMed ID: 15766694
[TBL] [Abstract][Full Text] [Related]
11. Automatic detection and segmentation of ground glass opacity nodules.
Zhou J; Chang S; Metaxas DN; Zhao B; Schwartz LH; Ginsberg MS
Med Image Comput Comput Assist Interv; 2006; 9(Pt 1):784-91. PubMed ID: 17354962
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. Computer-aided diagnostic scheme for the detection of lung nodules on chest radiographs: localized search method based on anatomical classification.
Shiraishi J; Li Q; Suzuki K; Engelmann R; Doi K
Med Phys; 2006 Jul; 33(7):2642-53. PubMed ID: 16898468
[TBL] [Abstract][Full Text] [Related]
14. Effect of CAD on radiologists' detection of lung nodules on thoracic CT scans: analysis of an observer performance study by nodule size.
Sahiner B; Chan HP; Hadjiiski LM; Cascade PN; Kazerooni EA; Chughtai AR; Poopat C; Song T; Frank L; Stojanovska J; Attili A
Acad Radiol; 2009 Dec; 16(12):1518-30. PubMed ID: 19896069
[TBL] [Abstract][Full Text] [Related]
15. A cascade and heterogeneous neural network for CT pulmonary nodule detection and its evaluation on both phantom and patient data.
Xiao Y; Wang X; Li Q; Fan R; Chen R; Shao Y; Chen Y; Gao Y; Liu A; Chen L; Liu S
Comput Med Imaging Graph; 2021 Jun; 90():101889. PubMed ID: 33848755
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Performance of a deep learning-based lung nodule detection system as an alternative reader in a Chinese lung cancer screening program.
Cui X; Zheng S; Heuvelmans MA; Du Y; Sidorenkov G; Fan S; Li Y; Xie Y; Zhu Z; Dorrius MD; Zhao Y; Veldhuis RNJ; de Bock GH; Oudkerk M; van Ooijen PMA; Vliegenthart R; Ye Z
Eur J Radiol; 2022 Jan; 146():110068. PubMed ID: 34871936
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Efficacy of computer-aided detection system and thin-slab maximum intensity projection technique in the detection of pulmonary nodules in patients with resected metastases.
Park EA; Goo JM; Lee JW; Kang CH; Lee HJ; Lee CH; Park CM; Lee HY; Im JG
Invest Radiol; 2009 Feb; 44(2):105-13. PubMed ID: 19034026
[TBL] [Abstract][Full Text] [Related]
20. Fully automatic detection of lung nodules in CT images using a hybrid feature set.
Shaukat F; Raja G; Gooya A; Frangi AF
Med Phys; 2017 Jul; 44(7):3615-3629. PubMed ID: 28409834
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]