235 related articles for article (PubMed ID: 30900029)
1. Glioma Tumor Grade Identification Using Artificial Intelligent Techniques.
Ahammed Muneer K V ; Rajendran VR; K PJ
J Med Syst; 2019 Mar; 43(5):113. PubMed ID: 30900029
[TBL] [Abstract][Full Text] [Related]
2. Glioma grading using a machine-learning framework based on optimized features obtained from T
Sengupta A; Ramaniharan AK; Gupta RK; Agarwal S; Singh A
J Magn Reson Imaging; 2019 Oct; 50(4):1295-1306. PubMed ID: 30895704
[TBL] [Abstract][Full Text] [Related]
3. [An artificial neural network model for glioma grading using image information].
Mao Y; Liao W; Cao D; Zhao L; Wu X; Kong L; Zhou G; Zhao Y; Wang D
Zhong Nan Da Xue Xue Bao Yi Xue Ban; 2018 Dec; 43(12):1315-1322. PubMed ID: 30643047
[TBL] [Abstract][Full Text] [Related]
4. Quantitative glioma grading using transformed gray-scale invariant textures of MRI.
Li-Chun Hsieh K; Chen CY; Lo CM
Comput Biol Med; 2017 Apr; 83():102-108. PubMed ID: 28254615
[TBL] [Abstract][Full Text] [Related]
5. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.
Zhang X; Yan LF; Hu YC; Li G; Yang Y; Han Y; Sun YZ; Liu ZC; Tian Q; Han ZY; Liu LD; Hu BQ; Qiu ZY; Wang W; Cui GB
Oncotarget; 2017 Jul; 8(29):47816-47830. PubMed ID: 28599282
[TBL] [Abstract][Full Text] [Related]
6. Machine learning applications to neuroimaging for glioma detection and classification: An artificial intelligence augmented systematic review.
Buchlak QD; Esmaili N; Leveque JC; Bennett C; Farrokhi F; Piccardi M
J Clin Neurosci; 2021 Jul; 89():177-198. PubMed ID: 34119265
[TBL] [Abstract][Full Text] [Related]
7. Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.
Nir G; Karimi D; Goldenberg SL; Fazli L; Skinnider BF; Tavassoli P; Turbin D; Villamil CF; Wang G; Thompson DJS; Black PC; Salcudean SE
JAMA Netw Open; 2019 Mar; 2(3):e190442. PubMed ID: 30848813
[TBL] [Abstract][Full Text] [Related]
8. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks.
Ertosun MG; Rubin DL
AMIA Annu Symp Proc; 2015; 2015():1899-908. PubMed ID: 26958289
[TBL] [Abstract][Full Text] [Related]
9. Imaging biomarker analysis of advanced multiparametric MRI for glioma grading.
Vamvakas A; Williams SC; Theodorou K; Kapsalaki E; Fountas K; Kappas C; Vassiou K; Tsougos I
Phys Med; 2019 Apr; 60():188-198. PubMed ID: 30910431
[TBL] [Abstract][Full Text] [Related]
10. An automatic glioma grading method based on multi-feature extraction and fusion.
Zhan T; Feng P; Hong X; Lu Z; Xiao L; Zhang Y
Technol Health Care; 2017 Jul; 25(S1):377-385. PubMed ID: 28582926
[TBL] [Abstract][Full Text] [Related]
11. Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: A pilot study.
Bacchi S; Zerner T; Dongas J; Asahina AT; Abou-Hamden A; Otto S; Oakden-Rayner L; Patel S
J Clin Neurosci; 2019 Dec; 70():11-13. PubMed ID: 31648967
[TBL] [Abstract][Full Text] [Related]
12. Machine learning methods for the classification of gliomas: Initial results using features extracted from MR spectroscopy.
Ranjith G; Parvathy R; Vikas V; Chandrasekharan K; Nair S
Neuroradiol J; 2015 Apr; 28(2):106-11. PubMed ID: 25923676
[TBL] [Abstract][Full Text] [Related]
13. A comparative study for glioma classification using deep convolutional neural networks.
Özcan H; Emiroğlu BG; Sabuncuoğlu H; Özdoğan S; Soyer A; Saygı T
Math Biosci Eng; 2021 Jan; 18(2):1550-1572. PubMed ID: 33757198
[TBL] [Abstract][Full Text] [Related]
14. Building medical image classifiers with very limited data using segmentation networks.
Wong KCL; Syeda-Mahmood T; Moradi M
Med Image Anal; 2018 Oct; 49():105-116. PubMed ID: 30119038
[TBL] [Abstract][Full Text] [Related]
15. Automatic glioma segmentation based on adaptive superpixel.
Wu Y; Zhao Z; Wu W; Lin Y; Wang M
BMC Med Imaging; 2019 Aug; 19(1):73. PubMed ID: 31443642
[TBL] [Abstract][Full Text] [Related]
16. 3D Deep Learning for Multi-modal Imaging-Guided Survival Time Prediction of Brain Tumor Patients.
Nie D; Zhang H; Adeli E; Liu L; Shen D
Med Image Comput Comput Assist Interv; 2016 Oct; 9901():212-220. PubMed ID: 28149967
[TBL] [Abstract][Full Text] [Related]
17. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma's grade and IDH status.
De Looze C; Beausang A; Cryan J; Loftus T; Buckley PG; Farrell M; Looby S; Reilly R; Brett F; Kearney H
J Neurooncol; 2018 Sep; 139(2):491-499. PubMed ID: 29770897
[TBL] [Abstract][Full Text] [Related]
18. Whole-brain amide proton transfer (APT) and nuclear overhauser enhancement (NOE) imaging in glioma patients using low-power steady-state pulsed chemical exchange saturation transfer (CEST) imaging at 7T.
Heo HY; Jones CK; Hua J; Yadav N; Agarwal S; Zhou J; van Zijl PC; Pillai JJ
J Magn Reson Imaging; 2016 Jul; 44(1):41-50. PubMed ID: 26663561
[TBL] [Abstract][Full Text] [Related]
19. Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI).
Khorasani A; Dadashi Serej N; Jalilian M; Shayganfar A; Tavakoli MB
Sci Rep; 2023 Oct; 13(1):17646. PubMed ID: 37848493
[TBL] [Abstract][Full Text] [Related]
20. Biopsy targeting gliomas: do functional imaging techniques identify similar target areas?
Weber MA; Henze M; Tüttenberg J; Stieltjes B; Meissner M; Zimmer F; Burkholder I; Kroll A; Combs SE; Vogt-Schaden M; Giesel FL; Zoubaa S; Haberkorn U; Kauczor HU; Essig M
Invest Radiol; 2010 Dec; 45(12):755-68. PubMed ID: 20829706
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
