290 related articles for article (PubMed ID: 32271288)
1. Deep Learning AI Applications in the Imaging of Glioma.
Zlochower A; Chow DS; Chang P; Khatri D; Boockvar JA; Filippi CG
Top Magn Reson Imaging; 2020 Apr; 29(2):115-0. PubMed ID: 32271288
[TBL] [Abstract][Full Text] [Related]
2. Updates on Deep Learning and Glioma: Use of Convolutional Neural Networks to Image Glioma Heterogeneity.
Chow DS; Khatri D; Chang PD; Zlochower A; Boockvar JA; Filippi CG
Neuroimaging Clin N Am; 2020 Nov; 30(4):493-503. PubMed ID: 33038999
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. 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]
5. 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]
6. A novel image signature-based radiomics method to achieve precise diagnosis and prognostic stratification of gliomas.
Luo H; Zhuang Q; Wang Y; Abudumijiti A; Shi K; Rominger A; Chen H; Yang Z; Tran V; Wu G; Li Z; Fan Z; Qi Z; Guo Y; Yu J; Shi Z
Lab Invest; 2021 Apr; 101(4):450-462. PubMed ID: 32829381
[TBL] [Abstract][Full Text] [Related]
7. Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.
Abd-Ellah MK; Awad AI; Khalaf AAM; Ibraheem AM
Sci Rep; 2024 Apr; 14(1):9501. PubMed ID: 38664436
[TBL] [Abstract][Full Text] [Related]
8. Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks.
Ge C; Gu IY; Jakola AS; Yang J
Annu Int Conf IEEE Eng Med Biol Soc; 2018 Jul; 2018():5894-5897. PubMed ID: 30441677
[TBL] [Abstract][Full Text] [Related]
9. Deep Transfer Learning and Radiomics Feature Prediction of Survival of Patients with High-Grade Gliomas.
Han W; Qin L; Bay C; Chen X; Yu KH; Miskin N; Li A; Xu X; Young G
AJNR Am J Neuroradiol; 2020 Jan; 41(1):40-48. PubMed ID: 31857325
[TBL] [Abstract][Full Text] [Related]
10. Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images.
Naceur MB; Saouli R; Akil M; Kachouri R
Comput Methods Programs Biomed; 2018 Nov; 166():39-49. PubMed ID: 30415717
[TBL] [Abstract][Full Text] [Related]
11. Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network.
Choi KS; Choi SH; Jeong B
Neuro Oncol; 2019 Sep; 21(9):1197-1209. PubMed ID: 31127834
[TBL] [Abstract][Full Text] [Related]
12. Recent Application of Advanced MR Imaging to Predict Pseudoprogression in High-grade Glioma Patients.
Yoo RE; Choi SH
Magn Reson Med Sci; 2016; 15(2):165-77. PubMed ID: 26726012
[TBL] [Abstract][Full Text] [Related]
13. Overall survival time prediction for glioblastoma using multimodal deep KNN.
Tang Z; Cao H; Xu Y; Yang Q; Wang J; Zhang H
Phys Med Biol; 2022 Jun; 67(13):. PubMed ID: 35533670
[TBL] [Abstract][Full Text] [Related]
14. An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.
Hoseini F; Shahbahrami A; Bayat P
J Digit Imaging; 2018 Oct; 31(5):738-747. PubMed ID: 29488179
[TBL] [Abstract][Full Text] [Related]
15. Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages.
Nie D; Lu J; Zhang H; Adeli E; Wang J; Yu Z; Liu L; Wang Q; Wu J; Shen D
Sci Rep; 2019 Jan; 9(1):1103. PubMed ID: 30705340
[TBL] [Abstract][Full Text] [Related]
16. Basic principles of mathematical growth modeling applied to high-grade gliomas: A brief clinical review for clinicians.
Cisneros-Sanchez AK; Flores-Alvarez E; Melendez-Mier G; Roldan-Valadez E
Neurol India; 2018; 66(6):1575-1583. PubMed ID: 30504543
[TBL] [Abstract][Full Text] [Related]
17. IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation.
Choi Y; Nam Y; Lee YS; Kim J; Ahn KJ; Jang J; Shin NY; Kim BS; Jeon SS
Eur J Radiol; 2020 Jul; 128():109031. PubMed ID: 32417712
[TBL] [Abstract][Full Text] [Related]
18. A quantitative study of shape descriptors from glioblastoma multiforme phenotypes for predicting survival outcome.
Chaddad A; Desrosiers C; Hassan L; Tanougast C
Br J Radiol; 2016 Dec; 89(1068):20160575. PubMed ID: 27781499
[TBL] [Abstract][Full Text] [Related]
19. Brain tumor segmentation with Deep Neural Networks.
Havaei M; Davy A; Warde-Farley D; Biard A; Courville A; Bengio Y; Pal C; Jodoin PM; Larochelle H
Med Image Anal; 2017 Jan; 35():18-31. PubMed ID: 27310171
[TBL] [Abstract][Full Text] [Related]
20. An enhanced deep learning approach for brain cancer MRI images classification using residual networks.
Abdelaziz Ismael SA; Mohammed A; Hefny H
Artif Intell Med; 2020 Jan; 102():101779. PubMed ID: 31980109
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]