184 related articles for article (PubMed ID: 29060752)
1. Content-based retrieval for lung nodule diagnosis using learned distance metric.
Guohui Wei ; He Ma ; Wei Qian ; Hongyang Jiang ; Xinzhuo Zhao
Annu Int Conf IEEE Eng Med Biol Soc; 2017 Jul; 2017():3910-3913. PubMed ID: 29060752
[TBL] [Abstract][Full Text] [Related]
2. Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric.
Wei G; Cao H; Ma H; Qi S; Qian W; Ma Z
J Med Syst; 2017 Nov; 42(1):13. PubMed ID: 29185058
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 3D gray density coding feature for benign-malignant pulmonary nodule classification on chest CT.
Zheng B; Yang D; Zhu Y; Liu Y; Hu J; Bai C
Med Phys; 2021 Dec; 48(12):7826-7836. PubMed ID: 34655238
[TBL] [Abstract][Full Text] [Related]
6. An integrated segmentation and shape-based classification scheme for distinguishing adenocarcinomas from granulomas on lung CT.
Alilou M; Beig N; Orooji M; Rajiah P; Velcheti V; Rakshit S; Reddy N; Yang M; Jacono F; Gilkeson RC; Linden P; Madabhushi A
Med Phys; 2017 Jul; 44(7):3556-3569. PubMed ID: 28295386
[TBL] [Abstract][Full Text] [Related]
7. A multi-feature image retrieval scheme for pulmonary nodule diagnosis.
Wei G; Qiu M; Zhang K; Li M; Wei D; Li Y; Liu P; Cao H; Xing M; Yang F
Medicine (Baltimore); 2020 Jan; 99(4):e18724. PubMed ID: 31977863
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. Example-based assisting approach for pulmonary nodule classification in three-dimensional thoracic computed tomography images.
Kawata Y; Niki N; Ohmatsu H; Moriyama N
Acad Radiol; 2003 Dec; 10(12):1402-15. PubMed ID: 14697008
[TBL] [Abstract][Full Text] [Related]
10. Data analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative.
Wang W; Luo J; Yang X; Lin H
Acad Radiol; 2015 Apr; 22(4):488-95. PubMed ID: 25601306
[TBL] [Abstract][Full Text] [Related]
11. Integrating PET and CT information to improve diagnostic accuracy for lung nodules: A semiautomatic computer-aided method.
Nie Y; Li Q; Li F; Pu Y; Appelbaum D; Doi K
J Nucl Med; 2006 Jul; 47(7):1075-80. PubMed ID: 16818939
[TBL] [Abstract][Full Text] [Related]
12. Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography.
Zhu Y; Tan Y; Hua Y; Wang M; Zhang G; Zhang J
J Digit Imaging; 2010 Feb; 23(1):51-65. PubMed ID: 19242759
[TBL] [Abstract][Full Text] [Related]
13. Computer-aided diagnosis of pulmonary nodules on CT scans: segmentation and classification using 3D active contours.
Way TW; Hadjiiski LM; Sahiner B; Chan HP; Cascade PN; Kazerooni EA; Bogot N; Zhou C
Med Phys; 2006 Jul; 33(7):2323-37. PubMed ID: 16898434
[TBL] [Abstract][Full Text] [Related]
14. A novel higher order appearance texture analysis to diagnose lung cancer based on a modified local ternary pattern.
Alksas A; Shaffie A; Ghazal M; Taher F; Khelifi A; Yaghi M; Soliman A; Bogaert EV; El-Baz A
Comput Methods Programs Biomed; 2023 Oct; 240():107692. PubMed ID: 37459773
[TBL] [Abstract][Full Text] [Related]
15. 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]
16. 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]
17. Measuring Interobserver Disagreement in Rating Diagnostic Characteristics of Pulmonary Nodule Using the Lung Imaging Database Consortium and Image Database Resource Initiative.
Lin H; Huang C; Wang W; Luo J; Yang X; Liu Y
Acad Radiol; 2017 Apr; 24(4):401-410. PubMed ID: 28169141
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. Computer-aided detection of pulmonary nodules: a comparative study using the public LIDC/IDRI database.
Jacobs C; van Rikxoort EM; Murphy K; Prokop M; Schaefer-Prokop CM; van Ginneken B
Eur Radiol; 2016 Jul; 26(7):2139-47. PubMed ID: 26443601
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
