232 related articles for article (PubMed ID: 32317574)
1. Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.
Fischer AM; Yacoub B; Savage RH; Martinez JD; Wichmann JL; Sahbaee P; Grbic S; Varga-Szemes A; Schoepf UJ
J Thorac Imaging; 2020 May; 35 Suppl 1():S21-S27. PubMed ID: 32317574
[TBL] [Abstract][Full Text] [Related]
2. Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers.
Fischer AM; Varga-Szemes A; Martin SS; Sperl JI; Sahbaee P; Neumann D; Gawlitza J; Henzler T; Johnson CM; Nance JW; Schoenberg SO; Schoepf UJ
J Thorac Imaging; 2020 May; 35 Suppl 1():S28-S34. PubMed ID: 32235188
[TBL] [Abstract][Full Text] [Related]
3. Artificial Intelligence Solutions for Analysis of X-ray Images.
Adams SJ; Henderson RDE; Yi X; Babyn P
Can Assoc Radiol J; 2021 Feb; 72(1):60-72. PubMed ID: 32757950
[TBL] [Abstract][Full Text] [Related]
4. Deep Convolutional Neural Network-based Software Improves Radiologist Detection of Malignant Lung Nodules on Chest Radiographs.
Sim Y; Chung MJ; Kotter E; Yune S; Kim M; Do S; Han K; Kim H; Yang S; Lee DJ; Choi BW
Radiology; 2020 Jan; 294(1):199-209. PubMed ID: 31714194
[TBL] [Abstract][Full Text] [Related]
5. An Optimized Superpixel Clustering Approach for High-Resolution Chest CT Image Segmentation.
Pinheiro da Rosa R; Cordeiro d'Ornellas M
Stud Health Technol Inform; 2015; 216():1045. PubMed ID: 26262344
[TBL] [Abstract][Full Text] [Related]
6. Identifying pulmonary nodules or masses on chest radiography using deep learning: external validation and strategies to improve clinical practice.
Liang CH; Liu YC; Wu MT; Garcia-Castro F; Alberich-Bayarri A; Wu FZ
Clin Radiol; 2020 Jan; 75(1):38-45. PubMed ID: 31521323
[TBL] [Abstract][Full Text] [Related]
7. Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.
Savage RH; van Assen M; Martin SS; Sahbaee P; Griffith LP; Giovagnoli D; Sperl JI; Hopfgartner C; Kärgel R; Schoepf UJ
J Thorac Imaging; 2020 May; 35 Suppl 1():S35-S39. PubMed ID: 32079905
[TBL] [Abstract][Full Text] [Related]
8. Computer analysis of computed tomography scans of the lung: a survey.
Sluimer I; Schilham A; Prokop M; van Ginneken B
IEEE Trans Med Imaging; 2006 Apr; 25(4):385-405. PubMed ID: 16608056
[TBL] [Abstract][Full Text] [Related]
9. Automated lung volumetry from routine thoracic CT scans: how reliable is the result?
Haas M; Hamm B; Niehues SM
Acad Radiol; 2014 May; 21(5):633-8. PubMed ID: 24703476
[TBL] [Abstract][Full Text] [Related]
10. Dual-energy CT of the lung.
Lu GM; Zhao Y; Zhang LJ; Schoepf UJ
AJR Am J Roentgenol; 2012 Nov; 199(5 Suppl):S40-53. PubMed ID: 23097167
[TBL] [Abstract][Full Text] [Related]
11. Developing and verifying automatic detection of active pulmonary tuberculosis from multi-slice spiral CT images based on deep learning.
Ma L; Wang Y; Guo L; Zhang Y; Wang P; Pei X; Qian L; Jaeger S; Ke X; Yin X; Lure FYM
J Xray Sci Technol; 2020; 28(5):939-951. PubMed ID: 32651351
[TBL] [Abstract][Full Text] [Related]
12. Contour-aware multi-label chest X-ray organ segmentation.
Kholiavchenko M; Sirazitdinov I; Kubrak K; Badrutdinova R; Kuleev R; Yuan Y; Vrtovec T; Ibragimov B
Int J Comput Assist Radiol Surg; 2020 Mar; 15(3):425-436. PubMed ID: 32034633
[TBL] [Abstract][Full Text] [Related]
13. Computer-aided diagnosis in chest radiography: beyond nodules.
van Ginneken B; Hogeweg L; Prokop M
Eur J Radiol; 2009 Nov; 72(2):226-30. PubMed ID: 19604661
[TBL] [Abstract][Full Text] [Related]
14. Radiation Dose Comparison Between 70 kVp and 100 kVp With Spectral Beam Shaping for Non-Contrast-Enhanced Pediatric Chest Computed Tomography: A Prospective Randomized Controlled Study.
Weis M; Henzler T; Nance JW; Haubenreisser H; Meyer M; Sudarski S; Schoenberg SO; Neff KW; Hagelstein C
Invest Radiol; 2017 Mar; 52(3):155-162. PubMed ID: 27662576
[TBL] [Abstract][Full Text] [Related]
15. Artificial intelligence applications for thoracic imaging.
Chassagnon G; Vakalopoulou M; Paragios N; Revel MP
Eur J Radiol; 2020 Feb; 123():108774. PubMed ID: 31841881
[TBL] [Abstract][Full Text] [Related]
16. Machine Learning and Deep Neural Networks: Applications in Patient and Scan Preparation, Contrast Medium, and Radiation Dose Optimization.
Eberhard M; Alkadhi H
J Thorac Imaging; 2020 May; 35 Suppl 1():S17-S20. PubMed ID: 32079904
[TBL] [Abstract][Full Text] [Related]
17. Deep Learning Applications in Chest Radiography and Computed Tomography: Current State of the Art.
Lee SM; Seo JB; Yun J; Cho YH; Vogel-Claussen J; Schiebler ML; Gefter WB; van Beek EJR; Goo JM; Lee KS; Hatabu H; Gee J; Kim N
J Thorac Imaging; 2019 Mar; 34(2):75-85. PubMed ID: 30802231
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Application of artificial intelligence in cardiac CT: From basics to clinical practice.
van den Oever LB; Vonder M; van Assen M; van Ooijen PMA; de Bock GH; Xie XQ; Vliegenthart R
Eur J Radiol; 2020 Jul; 128():108969. PubMed ID: 32361380
[TBL] [Abstract][Full Text] [Related]
20. Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.
Fischer AM; Varga-Szemes A; van Assen M; Griffith LP; Sahbaee P; Sperl JI; Nance JW; Schoepf UJ
AJR Am J Roentgenol; 2020 May; 214(5):1065-1071. PubMed ID: 32130041
[No Abstract] [Full Text] [Related]
[Next] [New Search]