421 related articles for article (PubMed ID: 29154123)
61. Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity.
Nishio M; Nagashima C
Acad Radiol; 2017 Mar; 24(3):328-336. PubMed ID: 28110797
[TBL] [Abstract][Full Text] [Related]
62. Computer-Aided Diagnosis (CAD) of Pulmonary Nodule of Thoracic CT Image Using Transfer Learning.
Zhang S; Sun F; Wang N; Zhang C; Yu Q; Zhang M; Babyn P; Zhong H
J Digit Imaging; 2019 Dec; 32(6):995-1007. PubMed ID: 31044393
[TBL] [Abstract][Full Text] [Related]
63. Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector.
Hao R; Qiang Y; Yan X
Comput Math Methods Med; 2018; 2018():2183847. PubMed ID: 29531575
[TBL] [Abstract][Full Text] [Related]
64. One-stage pulmonary nodule detection using 3-D DCNN with feature fusion and attention mechanism in CT image.
Huang YS; Chou PR; Chen HM; Chang YC; Chang RF
Comput Methods Programs Biomed; 2022 Jun; 220():106786. PubMed ID: 35398579
[TBL] [Abstract][Full Text] [Related]
65. 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]
66. Automatic lung nodule detection using multi-scale dot nodule-enhancement filter and weighted support vector machines in chest computed tomography.
Gu Y; Lu X; Zhang B; Zhao Y; Yu D; Gao L; Cui G; Wu L; Zhou T
PLoS One; 2019; 14(1):e0210551. PubMed ID: 30629724
[TBL] [Abstract][Full Text] [Related]
67. Computer-aided detection of lung nodules by SVM based on 3D matrix patterns.
Wang Q; Kang W; Wu C; Wang B
Clin Imaging; 2013; 37(1):62-9. PubMed ID: 23206609
[TBL] [Abstract][Full Text] [Related]
68. Pulmonary nodule detection in CT images based on shape constraint CV model.
Wang B; Tian X; Wang Q; Yang Y; Xie H; Zhang S; Gu L
Med Phys; 2015 Mar; 42(3):1241-54. PubMed ID: 25735280
[TBL] [Abstract][Full Text] [Related]
69. Doubling time calculations for lung cancer by three-dimensional computer-aided volumetry: effects of inter-observer differences and nodule characteristics.
Koike W; Iwano S; Matsuo K; Kitano M; Kawakami K; Naganawa S
J Med Imaging Radiat Oncol; 2014 Feb; 58(1):82-8. PubMed ID: 24304703
[TBL] [Abstract][Full Text] [Related]
70. Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans.
Lassen BC; Jacobs C; Kuhnigk JM; van Ginneken B; van Rikxoort EM
Phys Med Biol; 2015 Feb; 60(3):1307-23. PubMed ID: 25591989
[TBL] [Abstract][Full Text] [Related]
71. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: a complete intelligent system.
Keshani M; Azimifar Z; Tajeripour F; Boostani R
Comput Biol Med; 2013 May; 43(4):287-300. PubMed ID: 23369568
[TBL] [Abstract][Full Text] [Related]
72. 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]
73. Pulmonary Nodule Detection Model Based on SVM and CT Image Feature-Level Fusion with Rough Sets.
Zhou T; Lu H; Zhang J; Shi H
Biomed Res Int; 2016; 2016():8052436. PubMed ID: 27722173
[TBL] [Abstract][Full Text] [Related]
74. Automated detection of pulmonary nodules in PET/CT images: Ensemble false-positive reduction using a convolutional neural network technique.
Teramoto A; Fujita H; Yamamuro O; Tamaki T
Med Phys; 2016 Jun; 43(6):2821-2827. PubMed ID: 27277030
[TBL] [Abstract][Full Text] [Related]
75. LGDNet: local feature coupling global representations network for pulmonary nodules detection.
Chi J; Zhao J; Wang S; Yu X; Wu C
Med Biol Eng Comput; 2024 Jul; 62(7):1991-2004. PubMed ID: 38429443
[TBL] [Abstract][Full Text] [Related]
76. Lung nodule detection on thoracic computed tomography images: preliminary evaluation of a computer-aided diagnosis system.
Gurcan MN; Sahiner B; Petrick N; Chan HP; Kazerooni EA; Cascade PN; Hadjiiski L
Med Phys; 2002 Nov; 29(11):2552-8. PubMed ID: 12462722
[TBL] [Abstract][Full Text] [Related]
77. HoTPiG: a novel graph-based 3-D image feature set and its applications to computer-assisted detection of cerebral aneurysms and lung nodules.
Hanaoka S; Nomura Y; Takenaga T; Murata M; Nakao T; Miki S; Yoshikawa T; Hayashi N; Abe O; Shimizu A
Int J Comput Assist Radiol Surg; 2019 Dec; 14(12):2095-2107. PubMed ID: 30859456
[TBL] [Abstract][Full Text] [Related]
78. Computer-aided detection of pulmonary nodules using dynamic self-adaptive template matching and a FLDA classifier.
Gong J; Liu JY; Wang LJ; Zheng B; Nie SD
Phys Med; 2016 Dec; 32(12):1502-1509. PubMed ID: 27856118
[TBL] [Abstract][Full Text] [Related]
79. An analysis of early studies released by the Lung Imaging Database Consortium (LIDC).
Turner WD; Kelliher TP; Ross JC; Miller JV
Med Image Comput Comput Assist Interv; 2006; 9(Pt 2):487-94. PubMed ID: 17354808
[TBL] [Abstract][Full Text] [Related]
80. Nodule-CLIP: Lung nodule classification based on multi-modal contrastive learning.
Sun L; Zhang M; Lu Y; Zhu W; Yi Y; Yan F
Comput Biol Med; 2024 Jun; 175():108505. PubMed ID: 38688129
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
