141 related articles for article (PubMed ID: 36215857)
1. Robust multimodal fusion network using adversarial learning for brain tumor grading.
Jeong SW; Cho HH; Lee S; Park H
Comput Methods Programs Biomed; 2022 Nov; 226():107165. PubMed ID: 36215857
[TBL] [Abstract][Full Text] [Related]
2. Radiomics-Based Machine Learning Classification for Glioma Grading Using Diffusion- and Perfusion-Weighted Magnetic Resonance Imaging.
Hashido T; Saito S; Ishida T
J Comput Assist Tomogr; 2021 Jul-Aug 01; 45(4):606-613. PubMed ID: 34270479
[TBL] [Abstract][Full Text] [Related]
3. 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]
4. Grading diffuse gliomas without intense contrast enhancement by amide proton transfer MR imaging: comparisons with diffusion- and perfusion-weighted imaging.
Togao O; Hiwatashi A; Yamashita K; Kikuchi K; Keupp J; Yoshimoto K; Kuga D; Yoneyama M; Suzuki SO; Iwaki T; Takahashi M; Iihara K; Honda H
Eur Radiol; 2017 Feb; 27(2):578-588. PubMed ID: 27003139
[TBL] [Abstract][Full Text] [Related]
5. Joint learning-based feature reconstruction and enhanced network for incomplete multi-modal brain tumor segmentation.
Diao Y; Li F; Li Z
Comput Biol Med; 2023 Sep; 163():107234. PubMed ID: 37450967
[TBL] [Abstract][Full Text] [Related]
6. Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades.
Zhang Z; Xiao J; Wu S; Lv F; Gong J; Jiang L; Yu R; Luo T
J Digit Imaging; 2020 Aug; 33(4):826-837. PubMed ID: 32040669
[TBL] [Abstract][Full Text] [Related]
7. Common feature learning for brain tumor MRI synthesis by context-aware generative adversarial network.
Huang P; Li D; Jiao Z; Wei D; Cao B; Mo Z; Wang Q; Zhang H; Shen D
Med Image Anal; 2022 Jul; 79():102472. PubMed ID: 35567847
[TBL] [Abstract][Full Text] [Related]
8. Hierarchical-order multimodal interaction fusion network for grading gliomas.
He M; Han K; Zhang Y; Chen W
Phys Med Biol; 2021 Nov; 66(21):. PubMed ID: 34663762
[No Abstract] [Full Text] [Related]
9. Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based clustered images.
Inano R; Oishi N; Kunieda T; Arakawa Y; Kikuchi T; Fukuyama H; Miyamoto S
Sci Rep; 2016 Jul; 6():30344. PubMed ID: 27456199
[TBL] [Abstract][Full Text] [Related]
10. A simple model for glioma grading based on texture analysis applied to conventional brain MRI.
Suárez-García JG; Hernández-López JM; Moreno-Barbosa E; de Celis-Alonso B
PLoS One; 2020; 15(5):e0228972. PubMed ID: 32413034
[TBL] [Abstract][Full Text] [Related]
11. Intergrating conventional MRI, texture analysis of dynamic contrast-enhanced MRI, and susceptibility weighted imaging for glioma grading.
Su CQ; Lu SS; Han QY; Zhou MD; Hong XN
Acta Radiol; 2019 Jun; 60(6):777-787. PubMed ID: 30244590
[TBL] [Abstract][Full Text] [Related]
12. Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.
Qin JB; Liu Z; Zhang H; Shen C; Wang XC; Tan Y; Wang S; Wu XF; Tian J
Med Sci Monit; 2017 May; 23():2168-2178. PubMed ID: 28478462
[TBL] [Abstract][Full Text] [Related]
13. Textural features of dynamic contrast-enhanced MRI derived model-free and model-based parameter maps in glioma grading.
Xie T; Chen X; Fang J; Kang H; Xue W; Tong H; Cao P; Wang S; Yang Y; Zhang W
J Magn Reson Imaging; 2018 Apr; 47(4):1099-1111. PubMed ID: 28845594
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. Radiomics strategy for glioma grading using texture features from multiparametric MRI.
Tian Q; Yan LF; Zhang X; Zhang X; Hu YC; Han Y; Liu ZC; Nan HY; Sun Q; Sun YZ; Yang Y; Yu Y; Zhang J; Hu B; Xiao G; Chen P; Tian S; Xu J; Wang W; Cui GB
J Magn Reson Imaging; 2018 Dec; 48(6):1518-1528. PubMed ID: 29573085
[TBL] [Abstract][Full Text] [Related]
16. Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities.
Zhou T; Canu S; Vera P; Ruan S
IEEE Trans Image Process; 2021; 30():4263-4274. PubMed ID: 33830924
[TBL] [Abstract][Full Text] [Related]
17. 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]
18. Ki-67 labeling index and the grading of cerebral gliomas by using intravoxel incoherent motion diffusion-weighted imaging and three-dimensional arterial spin labeling magnetic resonance imaging.
Wang C; Dong H
Acta Radiol; 2020 Aug; 61(8):1057-1063. PubMed ID: 31830431
[TBL] [Abstract][Full Text] [Related]
19. Glioma grading prediction using multiparametric magnetic resonance imaging-based radiomics combined with proton magnetic resonance spectroscopy and diffusion tensor imaging.
Lin K; Cidan W; Qi Y; Wang X
Med Phys; 2022 Jul; 49(7):4419-4429. PubMed ID: 35366379
[TBL] [Abstract][Full Text] [Related]
20. Preoperative grading of supratentorial nonenhancing gliomas by high b-value diffusion-weighted 3 T magnetic resonance imaging.
Han H; Han C; Wu X; Zhong S; Zhuang X; Tan G; Wu H
J Neurooncol; 2017 May; 133(1):147-154. PubMed ID: 28439776
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
