153 related articles for article (PubMed ID: 30444907)
1. Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and experts' knowledge.
Kawagishi M; Kubo T; Sakamoto R; Yakami M; Fujimoto K; Aoyama G; Emoto Y; Sekiguchi H; Sakai K; Iizuka Y; Nishio M; Yamamoto H; Togashi K
PLoS One; 2018; 13(11):e0207661. PubMed ID: 30444907
[TBL] [Abstract][Full Text] [Related]
2. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.
Hirose T; Nitta N; Shiraishi J; Nagatani Y; Takahashi M; Murata K
Acad Radiol; 2008 Dec; 15(12):1505-12. PubMed ID: 19000867
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists.
Kawagishi M; Chen B; Furukawa D; Sekiguchi H; Sakai K; Kubo T; Yakami M; Fujimoto K; Sakamoto R; Emoto Y; Aoyama G; Iizuka Y; Nakagomi K; Yamamoto H; Togashi K
Int J Comput Assist Radiol Surg; 2017 May; 12(5):767-776. PubMed ID: 28285338
[TBL] [Abstract][Full Text] [Related]
5. Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience.
Shiraishi J; Abe H; Engelmann R; Aoyama M; MacMahon H; Doi K
Radiology; 2003 May; 227(2):469-74. PubMed ID: 12732700
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Is the computer-aided detection scheme for lung nodule also useful in detecting lung cancer?
Goo JM; Kim HY; Lee JW; Lee HJ; Lee CH; Lee KW; Kim TJ; Lim KY; Park SH; Bae KT
J Comput Assist Tomogr; 2008; 32(4):570-5. PubMed ID: 18664845
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Computer-aided diagnosis for the detection and classification of lung cancers on chest radiographs ROC analysis of radiologists' performance.
Shiraishi J; Abe H; Li F; Engelmann R; MacMahon H; Doi K
Acad Radiol; 2006 Aug; 13(8):995-1003. PubMed ID: 16843852
[TBL] [Abstract][Full Text] [Related]
10. Single-view 2D CNNs with fully automatic non-nodule categorization for false positive reduction in pulmonary nodule detection.
Eun H; Kim D; Jung C; Kim C
Comput Methods Programs Biomed; 2018 Oct; 165():215-224. PubMed ID: 30337076
[TBL] [Abstract][Full Text] [Related]
11. Solid, part-solid, or non-solid?: classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system.
Jacobs C; van Rikxoort EM; Scholten ET; de Jong PA; Prokop M; Schaefer-Prokop C; van Ginneken B
Invest Radiol; 2015 Mar; 50(3):168-73. PubMed ID: 25478740
[TBL] [Abstract][Full Text] [Related]
12. Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization.
Nishio M; Nishizawa M; Sugiyama O; Kojima R; Yakami M; Kuroda T; Togashi K
PLoS One; 2018; 13(4):e0195875. PubMed ID: 29672639
[TBL] [Abstract][Full Text] [Related]
13. Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance.
Awai K; Murao K; Ozawa A; Komi M; Hayakawa H; Hori S; Nishimura Y
Radiology; 2004 Feb; 230(2):347-52. PubMed ID: 14752180
[TBL] [Abstract][Full Text] [Related]
14. Effect of a computer-aided diagnosis scheme on radiologists' performance in detection of lung nodules on radiographs.
Kobayashi T; Xu XW; MacMahon H; Metz CE; Doi K
Radiology; 1996 Jun; 199(3):843-8. PubMed ID: 8638015
[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. 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]
17. Improving radiologists' recommendations with computer-aided diagnosis for management of small nodules detected by CT.
Li F; Li Q; Engelmann R; Aoyama M; Sone S; MacMahon H; Doi K
Acad Radiol; 2006 Aug; 13(8):943-50. PubMed ID: 16843846
[TBL] [Abstract][Full Text] [Related]
18. Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer.
Dhara AK; Mukhopadhyay S; Dutta A; Garg M; Khandelwal N
J Digit Imaging; 2017 Feb; 30(1):63-77. PubMed ID: 27678255
[TBL] [Abstract][Full Text] [Related]
19. A quadratic model for combining quantitative diagnostic assessments from radiologist and computer in computer-aided diagnosis.
Jiang Y; Metz CE
Acad Radiol; 2006 Feb; 13(2):140-51. PubMed ID: 16428048
[TBL] [Abstract][Full Text] [Related]
20. Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.
Cascio D; Magro R; Fauci F; Iacomi M; Raso G
Comput Biol Med; 2012 Nov; 42(11):1098-109. PubMed ID: 23020972
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
