1364 related articles for article (PubMed ID: 16156352)
1. 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]
2. How can a massive training artificial neural network (MTANN) be trained with a small number of cases in the distinction between nodules and vessels in thoracic CT?
Suzuki K; Doi K
Acad Radiol; 2005 Oct; 12(10):1333-41. PubMed ID: 16179210
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network.
Suzuki K; Shiraishi J; Abe H; MacMahon H; Doi K
Acad Radiol; 2005 Feb; 12(2):191-201. PubMed ID: 15721596
[TBL] [Abstract][Full Text] [Related]
5. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN).
Suzuki K; Abe H; MacMahon H; Doi K
IEEE Trans Med Imaging; 2006 Apr; 25(4):406-16. PubMed ID: 16608057
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. Computerized scheme for determination of the likelihood measure of malignancy for pulmonary nodules on low-dose CT images.
Aoyama M; Li Q; Katsuragawa S; Li F; Sone S; Doi K
Med Phys; 2003 Mar; 30(3):387-94. PubMed ID: 12674239
[TBL] [Abstract][Full Text] [Related]
8. A new method based on MTANNs for cutting down false-positives: an evaluation on different versions of commercial pulmonary nodule detection CAD software.
Shi Z; Si C; Feng Y; He L; Suzuki K
Biomed Mater Eng; 2014; 24(6):2839-46. PubMed ID: 25226989
[TBL] [Abstract][Full Text] [Related]
9. Neural network ensemble-based computer-aided diagnosis for differentiation of lung nodules on CT images: clinical evaluation.
Chen H; Xu Y; Ma Y; Ma B
Acad Radiol; 2010 May; 17(5):595-602. PubMed ID: 20167513
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. 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]
12. 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]
13. 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]
14. Mixture of expert 3D massive-training ANNs for reduction of multiple types of false positives in CAD for detection of polyps in CT colonography.
Suzuki K; Yoshida H; Näppi J; Armato SG; Dachman AH
Med Phys; 2008 Feb; 35(2):694-703. PubMed ID: 18383691
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. A novel approach to nodule feature optimization on thin section thoracic CT.
Samala R; Moreno W; You Y; Qian W
Acad Radiol; 2009 Apr; 16(4):418-27. PubMed ID: 19268853
[TBL] [Abstract][Full Text] [Related]
17. Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.
Nakamura K; Yoshida H; Engelmann R; MacMahon H; Katsuragawa S; Ishida T; Ashizawa K; Doi K
Radiology; 2000 Mar; 214(3):823-30. PubMed ID: 10715052
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Computer-aided detection of solid lung nodules in lossy compressed multidetector computed tomography chest exams.
Raffy P; Gaudeau Y; Miller DP; Moureaux JM; Castellino RA
Acad Radiol; 2006 Oct; 13(10):1194-203. PubMed ID: 16979068
[TBL] [Abstract][Full Text] [Related]
20. Massive-training artificial neural network coupled with Laplacian-eigenfunction-based dimensionality reduction for computer-aided detection of polyps in CT colonography.
Suzuki K; Zhang J; Xu J
IEEE Trans Med Imaging; 2010 Nov; 29(11):1907-17. PubMed ID: 20570766
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
