246 related articles for article (PubMed ID: 34164281)
1. Screen-detected solid nodules: from detection of nodule to structured reporting.
Silva M; Milanese G; Ledda RE; Pastorino U; Sverzellati N
Transl Lung Cancer Res; 2021 May; 10(5):2335-2346. PubMed ID: 34164281
[TBL] [Abstract][Full Text] [Related]
2. Detection of Subsolid Nodules in Lung Cancer Screening: Complementary Sensitivity of Visual Reading and Computer-Aided Diagnosis.
Silva M; Schaefer-Prokop CM; Jacobs C; Capretti G; Ciompi F; van Ginneken B; Pastorino U; Sverzellati N
Invest Radiol; 2018 Aug; 53(8):441-449. PubMed ID: 29543693
[TBL] [Abstract][Full Text] [Related]
3. The effect of radiation dose reduction on computer-aided detection (CAD) performance in a low-dose lung cancer screening population.
Young S; Lo P; Kim G; Brown M; Hoffman J; Hsu W; Wahi-Anwar W; Flores C; Lee G; Noo F; Goldin J; McNitt-Gray M
Med Phys; 2017 Apr; 44(4):1337-1346. PubMed ID: 28122122
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. LNDb challenge on automatic lung cancer patient management.
Pedrosa J; Aresta G; Ferreira C; Atwal G; Phoulady HA; Chen X; Chen R; Li J; Wang L; Galdran A; Bouchachia H; Kaluva KC; Vaidhya K; Chunduru A; Tarai S; Nadimpalli SPP; Vaidya S; Kim I; Rassadin A; Tian Z; Sun Z; Jia Y; Men X; Ramos I; Cunha A; Campilho A
Med Image Anal; 2021 May; 70():102027. PubMed ID: 33740739
[TBL] [Abstract][Full Text] [Related]
6. Quantitative nodule detection in low dose chest CT scans: new template modeling and evaluation for CAD system design.
Farag AA; El-Baz A; Gimelfarb G; El-Ghar MA; Eldiasty T
Med Image Comput Comput Assist Interv; 2005; 8(Pt 1):720-8. PubMed ID: 16685910
[TBL] [Abstract][Full Text] [Related]
7. Detection and size measurements of pulmonary nodules in ultra-low-dose CT with iterative reconstruction compared to low dose CT.
Sui X; Meinel FG; Song W; Xu X; Wang Z; Wang Y; Jin Z; Chen J; Vliegenthart R; Schoepf UJ
Eur J Radiol; 2016 Mar; 85(3):564-70. PubMed ID: 26860668
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists.
Li L; Liu Z; Huang H; Lin M; Luo D
Thorac Cancer; 2019 Feb; 10(2):183-192. PubMed ID: 30536611
[TBL] [Abstract][Full Text] [Related]
11. Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography.
Ziyad SR; Radha V; Vayyapuri T
Curr Med Imaging Rev; 2020; 16(1):16-26. PubMed ID: 31989890
[TBL] [Abstract][Full Text] [Related]
12. Shape-based computer-aided detection of lung nodules in thoracic CT images.
Ye X; Lin X; Dehmeshki J; Slabaugh G; Beddoe G
IEEE Trans Biomed Eng; 2009 Jul; 56(7):1810-20. PubMed ID: 19527950
[TBL] [Abstract][Full Text] [Related]
13. Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy.
Zhang H; Ma J; Wang J; Moore W; Liang Z
Med Phys; 2017 Sep; 44(9):e264-e278. PubMed ID: 28901622
[TBL] [Abstract][Full Text] [Related]
14. Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.
Singh R; Kalra MK; Homayounieh F; Nitiwarangkul C; McDermott S; Little BP; Lennes IT; Shepard JO; Digumarthy SR
Quant Imaging Med Surg; 2021 Apr; 11(4):1134-1143. PubMed ID: 33816155
[TBL] [Abstract][Full Text] [Related]
15. Outstanding negative prediction performance of solid pulmonary nodule volume AI for ultra-LDCT baseline lung cancer screening risk stratification.
Lancaster HL; Zheng S; Aleshina OO; Yu D; Yu Chernina V; Heuvelmans MA; de Bock GH; Dorrius MD; Gratama JW; Morozov SP; Gombolevskiy VA; Silva M; Yi J; Oudkerk M
Lung Cancer; 2022 Mar; 165():133-140. PubMed ID: 35123156
[TBL] [Abstract][Full Text] [Related]
16. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: three decades' development course and future prospect.
Liu B; Chi W; Li X; Li P; Liang W; Liu H; Wang W; He J
J Cancer Res Clin Oncol; 2020 Jan; 146(1):153-185. PubMed ID: 31786740
[TBL] [Abstract][Full Text] [Related]
17. Reducing False-Positives in Lung Nodules Detection Using Balanced Datasets.
Liang J; Ye G; Guo J; Huang Q; Zhang S
Front Public Health; 2021; 9():671070. PubMed ID: 34095073
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. A CAD system for pulmonary nodule prediction based on deep three-dimensional convolutional neural networks and ensemble learning.
Huang W; Xue Y; Wu Y
PLoS One; 2019; 14(7):e0219369. PubMed ID: 31299053
[TBL] [Abstract][Full Text] [Related]
20.
; ; . PubMed ID:
[No Abstract] [Full Text] [Related]
[Next] [New Search]
