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Davatzikos, C.[Christos],
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IEEE DOI
1102
BibRef
Gooya, A.,
Pohl, K.M.,
Bilello, M.,
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Biros, G.,
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Davatzikos, C.,
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IEEE DOI
1210
BibRef
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Stretton, E.[Erin],
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Tumor growth parameters estimation and source localization from a
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Elsevier DOI
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Diffusivity ratio; Source estimation; Eikonal equation;
Reaction-diffusion glioma growth modeling
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A Patch-Based Approach for the Segmentation of Pathologies:
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IEEE DOI
1604
biomedical MRI
BibRef
Sivakumar, P.,
Ganeshkumar, P.,
CANFIS based glioma brain tumor classification and retrieval system
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IJIST(27), No. 2, 2017, pp. 109-117.
DOI Link
1706
brain tumor, CANFIS, classification, retrieval, segmentation
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Pioro, E.P.[Erik P.],
Prabhakar Rao, B.V.V.S.N.,
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1708
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brain tumors, classifier, features, glioma, image fusion
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IEEE DOI
1808
Feature extraction, Tumors, Imaging, Brain, Pathology,
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classifier, glioma tumor, graph cut approach,
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BibRef
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DOI Link
2002
deep convolutional neural networks,
Jaya optimization algorithm, multimodal MRI brain images,
severity-level classification
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Dogra, J.[Jyotsna],
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1912
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Mahesh, K.M.[K. Michael],
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2002
brain tumor, CNN, deep learning, gliomas, image segmentation
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Elsevier DOI
2004
Brain tumor segmentation, Glioma, Neoplasia, Magnetic resonance imaging (MRI)
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Zhang, W.X.[Wen-Xue],
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High-resolution 3D imaging of microvascular architecture in human
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2005
glioma, microthrombi, microvascular architecture,
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Gliomas, Classification, Deep learning, ResNet, Dilated convolution
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BraTS, deep learning, glioma tumor, neural networks,
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Mohamed, L.A.[Linda Ait],
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2102
glioma tumor, random forest, random walk, segmentation, superpixels
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Lohmann, P.[Philipp],
Caldeira, L.[Liliana],
Brambilla, C.R.[Cláudia Régio],
Tellmann, L.[Lutz],
Scheins, J.[Jürgen],
Kops, E.R.[Elena Rota],
Galldiks, N.[Norbert],
Langen, K.J.[Karl-Josef],
Herzog, H.[Hans],
Shah, N.J.[N. Jon],
A Linearized Fit Model for Robust Shape Parameterization of FET-PET
TACs,
MedImg(40), No. 7, July 2021, pp. 1852-1862.
IEEE DOI
2107
Neuroscience, Kinetic theory, Tumors, Brain modeling, Shape, Imaging,
Field effect transistors, FET PET, glioma classification, parametric imaging
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Mathiyalagan, G.[Gomathi],
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A machine learning classification approach based glioma brain tumor
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DOI Link
2108
features, fuzzy logic, glioma, ridgelet filter, tumor
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Barzegar, Z.[Zeynab],
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IET-CV(15), No. 7, 2021, pp. 463-473.
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2109
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Singh, R.[Rahul],
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DOI Link
2112
Gabor filter bank,
Gaussian radial basis function-support vector machine (GRBF-SVM),
synthetic minority over-sampling technique (SMOTE)
BibRef
Liu, Z.H.[Zhi-Hua],
Tong, L.[Lei],
Chen, L.[Long],
Zhou, F.X.[Fei-Xiang],
Jiang, Z.H.[Zhe-Heng],
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Wang, Y.H.[Yin-Hai],
Shan, C.F.[Cai-Feng],
Li, L.[Ling],
Zhou, H.Y.[Hui-Yu],
CANet: Context Aware Network for Brain Glioma Segmentation,
MedImg(40), No. 7, July 2021, pp. 1763-1777.
IEEE DOI
2107
Image segmentation, Tumors, Graph neural networks,
Feature extraction, image segmentation
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Boudrioua, A.[Asma],
Aloui, A.[Abdelouahab],
Solaiman, B.[Basel],
Asli, L.[Larbi],
Ben Salem, D.[Douraied],
Tliba, S.[Souhil],
Automatic three-dimensional detection and volume estimation of
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DOI Link
2108
bounding box, edge indicator term,
fuzzy preference optimization model, level set method, segmentation
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Hedyehzadeh, M.[Mohammadreza],
Maghooli, K.[Keivan],
MomenGharibvand, M.[Mohammad],
Glioma grade detection using grasshopper optimization
algorithm-optimized machine learning methods:
The Cancer Imaging Archive study,
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DOI Link
2108
fuzzy C-means, GLCM, glioma grade,
grasshopper optimization algorithm, local binary pattern
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Abo Elenein, N.M.[Nagwa M.],
Piao, S.H.[Song-Hao],
Noor, A.[Alam],
Ahmed, P.N.[Pir Noman],
MIRAU-Net: An improved neural network based on U-Net for gliomas
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SP:IC(101), 2022, pp. 116553.
Elsevier DOI
2201
Brain tumor segmentation, U-Net, Full convolutional network,
Inception, Residual Module, Attention Gate
BibRef
Jiang, L.Q.[Lin-Qi],
Ning, C.Y.[Chun-Yu],
Li, J.Y.[Jing-Yang],
Glioma classification framework based on SE-ResNeXt network and its
optimization,
IET-IPR(16), No. 2, 2022, pp. 596-605.
DOI Link
2201
BibRef
Fang, Y.[Ying],
Huang, H.[He],
Yang, W.J.[Wei-Ji],
Xu, X.M.[Xiao-Mei],
Jiang, W.W.[Wei-Wei],
Lai, X.B.[Xiao-Bo],
Nonlocal convolutional block attention module VNet for gliomas
automatic segmentation,
IJIST(32), No. 2, 2022, pp. 528-543.
DOI Link
2203
automatic segmentation, convolutional block attention module,
deep learning, glioma, nonlocal block
BibRef
Paul, R.[Rahul],
Topological features in addition to radiomics signature predict 1P19Q
status and tumor grade in low-grade gliomas,
IJIST(32), No. 3, 2022, pp. 753-766.
DOI Link
2205
1p19q codeletion, low grade glioma, radiomics,
topological data analysis, topological features, tumor grade
BibRef
Cheng, J.H.[Jian-Hong],
Liu, J.[Jin],
Kuang, H.[Hulin],
Wang, J.X.[Jian-Xin],
A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma
Segmentation and IDH Genotyping,
MedImg(41), No. 6, June 2022, pp. 1520-1532.
IEEE DOI
2206
Image segmentation, Multitasking, Task analysis,
Magnetic resonance imaging, Feature extraction, Tumors,
transformer
BibRef
Gore, S.[Sonal],
Jagtap, J.[Jayant],
IDH-Based Radiogenomic Characterization of Glioma Using Local Ternary
Pattern Descriptor Integrated with Radiographic Features and Random
Forest Classifier,
IJIG(22), No. 3 2022, pp. 2140013.
DOI Link
2206
BibRef
Xiao, A.[Anqi],
Shen, B.[Biluo],
Shi, X.J.[Xiao-Jing],
Zhang, Z.[Zhe],
Zhang, Z.[Zeyu],
Tian, J.[Jie],
Ji, N.[Nan],
Hu, Z.H.[Zhen-Hua],
Intraoperative Glioma Grading Using Neural Architecture Search and
Multi-Modal Imaging,
MedImg(41), No. 10, October 2022, pp. 2570-2581.
IEEE DOI
2210
Imaging, Fluorescence, Feature extraction, Surgery,
Biomedical imaging, Medical diagnostic imaging, Deep learning,
NIR-II fluorescence imaging
BibRef
Hussain, S.S.[Syed Sajid],
Sachdeva, J.[Jainy],
Ahuja, C.K.[Chirag Kamal],
Singh, A.[Abhiav],
Enc-Unet: A novel method for Glioma segmentation,
IJIST(33), No. 2, 2023, pp. 465-482.
DOI Link
2303
3D-Unet, autoencoders, deep learning, glioma segmentation, MRI
BibRef
Gudigar, A.[Anjan],
Raghavendra, U.,
Rao, T.N.[Tejaswi N.],
Samanth, J.[Jyothi],
Rajinikanth, V.[Venkatesan],
Satapathy, S.C.[Suresh Chandra],
Ciaccio, E.J.[Edward J.],
Yee, C.W.[Chan Wai],
Acharya, U.R.[U. Rajendra],
FFCAEs: An efficient feature fusion framework using cascaded
autoencoders for the identification of gliomas,
IJIST(33), No. 2, 2023, pp. 483-494.
DOI Link
2303
cascaded autoencoders, classification accuracy,
computer-aided diagnostic tool, feature fusion framework,
magnetic resonance (MR)
BibRef
Malhotra, R.[Radhika],
Singh-Saini, B.[Barjinder],
Gupta, S.[Savita],
CB-D2RNet: An efficient context bridge network for glioma
segmentation,
JVCIR(94), 2023, pp. 103836.
Elsevier DOI
2306
MRI, Context bridge, Loss function, Dilated convolution
BibRef
Akram, M.T.[Muhammad Tahir],
Asghar, S.[Sohail],
Shahid, A.R.[Ahmad Raza],
Effective data augmentation for brain tumor segmentation,
IJIST(33), No. 4, 2023, pp. 1247-1260.
DOI Link
2307
2D U-Nets, brain tumor segmentation, data augmentation, glioma segmentation
BibRef
Liang, B.S.[Bao-Shan],
Tan, J.Y.[Jing-Ye],
Lozenski, L.[Luke],
Hormuth, D.A.[David A.],
Yankeelov, T.E.[Thomas E.],
Villa, U.[Umberto],
Faghihi, D.[Danial],
Bayesian Inference of Tissue Heterogeneity for Individualized
Prediction of Glioma Growth,
MedImg(42), No. 10, October 2023, pp. 2865-2875.
IEEE DOI
2310
BibRef
Ayadi, W.[Wadhah],
Elhamzi, W.[Wajdi],
Atri, M.[Mohamed],
A deep conventional neural network model for glioma tumor
segmentation,
IJIST(33), No. 5, 2023, pp. 1593-1605.
DOI Link
2310
brain, BraTS, CNN, medical diagnosis, MRI, segmentation, tumor
BibRef
Yin, Z.M.[Zi-Ming],
Gao, H.Y.[Hong-Yu],
Gong, J.C.[Jin-Chang],
Wang, Y.J.[Yuan-Jun],
WD-UNeXt: Weight loss function and dropout U-Net with ConvNeXt for
automatic segmentation of few shot brain gliomas,
IET-IPR(17), No. 11, 2023, pp. 3271-3280.
DOI Link
2310
brain gliomas, ConvNeXt, deep learning, MRI, U-Net
BibRef
Wei, Y.[Yiran],
Chen, X.[Xi],
Zhu, L.[Lei],
Zhang, L.[Lipei],
Schönlieb, C.B.[Carola-Bibiane],
Price, S.[Stephen],
Li, C.[Chao],
Multi-Modal Learning for Predicting the Genotype of Glioma,
MedImg(42), No. 11, November 2023, pp. 3167-3178.
IEEE DOI
2311
BibRef
Wang, H.F.[Hong-Fei],
Peng, X.[Xinhao],
Ma, S.Q.[Shi-Qing],
Wang, S.[Shuai],
Xu, C.[Chuan],
Yang, P.[Ping],
An adaptive enhancement method based on stochastic parallel gradient
descent of glioma image,
IET-IPR(17), No. 14, 2023, pp. 3976-3985.
DOI Link
2312
image denoising, image enhancement, parallel algorithms,
artefacts suppression, contrast improvement,
stochastic parallel gradient descent
BibRef
van Garderen, K.A.[Karin A.],
van der Voort, S.R.[Sebastian R.],
Wijnenga, M.M.J.[Maarten M. J.],
Incekara, F.[Fatih],
Alafandi, A.[Ahmad],
Kapsas, G.[Georgios],
Gahrmann, R.[Renske],
Schouten, J.W.[Joost W.],
Dubbink, H.J.[Hendrikus J.],
Vincent, A.J.P.E.[Arnaud J. P. E.],
van den Bent, M.[Martin],
French, P.J.[Pim J.],
Smits, M.[Marion],
Klein, S.[Stefan],
Evaluating the Predictive Value of Glioma Growth Models for Low-Grade
Glioma After Tumor Resection,
MedImg(43), No. 1, January 2024, pp. 253-263.
IEEE DOI
2401
BibRef
Karun, B.[Baiju],
Prasath, T.A.[T. Arun],
Rajasekaran, M.P.[M. Pallikonda],
Makreri, R.[Rakhee],
Glioma detection using EHO based FLAME clustering in MR brain images,
IJIST(34), No. 1, 2024, pp. e22937.
DOI Link
2401
BraTS dataset, deep learning (DL),
elephant herding optimization (EHO), tumour segmentation
BibRef
Zhipeng, L.[Liu],
Jiawei, W.[Wu],
Ye, J.[Jing],
Bian, X.F.[Xue-Feng],
Qiwei, W.[Wu],
Li, R.[Rui],
Zhu, Y.[Yinxing],
ADT-UNet: An Innovative Algorithm for Glioma Segmentation in MR
Images,
IJIST(34), No. 5, 2024, pp. e23150.
DOI Link
2408
deep learning, glioma, image segmentation, Transformer, U-Net
BibRef
Sachdeva, J.[Jainy],
Sharma, D.[Deepanshu],
Ahuja, C.K.[Chirag Kamal],
Multiscale segmentation net for segregating heterogeneous brain
tumors: Gliomas on multimodal MR images,
IVC(149), 2024, pp. 105191.
Elsevier DOI
2408
Magnetic resonance imaging (MRI), Semantic segmentation,
Brain tumor, Deep convolutional neural network (DCNN),
Multi-scale feature extraction
BibRef
Dequidt, P.[Paul],
Bourdon, P.[Pascal],
Tremblais, B.[Benoit],
Guillevin, C.[Carole],
Gianelli, B.[Benoit],
Boutet, C.[Claire],
Cottier, J.P.[Jean-Philippe],
Vallée, J.N.[Jean-Noël],
Fernandez-Maloigne, C.[Christine],
Guillevin, R.[Rémy],
Assigning a new glioma grade label ground-truth for the BraTS dataset
using radiologic criteria,
IPTA20(1-6)
IEEE DOI
2206
Sociology, Magnetic resonance, Organizations, Tools, Oncology,
Statistics, Tumors, Glioma grading, machine learning,
radiomics
BibRef
Brzenczek, C.[Cyril],
Mézières, S.[Sophie],
Gaudeau, Y.[Yann],
Blonski, M.[Marie],
Rech, F.[Fabien],
Obara, T.[Tiphaine],
Taillandier, L.[Luc],
Moureaux, J.M.[Jean-Marie],
An original MRI-based method to quantify the Diffuse Low-Grade Glioma
brain infiltration,
IPTA20(1-6)
IEEE DOI
2206
Magnetic resonance imaging, Standards organizations, Morphology,
Medical treatment, Organizations, Tools, Tumors, Brain,
Segmentation
BibRef
Li, X.[Xuhui],
Xu, Y.[Yong],
Xiang, F.[Feng],
Liu, Q.[Qing],
Huang, W.H.[Wei-Hong],
Xie, B.[Bin],
KINET: A Non-Invasive Method for Predicting Ki67 Index of Glioma,
ICIP21(150-154)
IEEE DOI
2201
Deep learning, Training, Magnetic resonance imaging, Surgery,
Medical services, Metadata, Ki67 index, KiNet, multi-modal, glioma, MRI
BibRef
Ali, M.B.[Muhaddisa Barat],
Gu, I.Y.H.[Irene Yu-Hua],
Jakola, A.S.[Asgeir Store],
Multi-stream Convolutional Autoencoder and 2D Generative Adversarial
Network for Glioma Classification,
CAIP19(I:234-245).
Springer DOI
1909
BibRef
Ge, C.J.[Chen-Jie],
Gu, I.Y.H.[Irene Yu-Hua],
Jakola, A.S.[Asgeir Store],
Yang, J.[Jie],
Cross-Modality Augmentation of Brain MR Images Using a Novel Pairwise
Generative Adversarial Network for Enhanced Glioma Classification,
ICIP19(559-563)
IEEE DOI
1910
Pairwise generative adversarial network (GAN),
cross-modality image augmentation, glioma classification,
MR images
BibRef
Naudin, M.[Mathieu],
Tremblais, B.[Benoit],
Guillevin, C.[Carole],
Guillevin, R.[Rémy],
Fernandez-Maloigne, C.[Christine],
Diffuse Low Grade Glioma NMR Assessment for Better Intra-operative
Targeting Using Fuzzy Logic,
ACIVS18(200-210).
Springer DOI
1810
BibRef
Ge, C.,
Qu, Q.,
Gu, I.Y.,
Store Jakola, A.,
3D Multi-Scale Convolutional Networks for Glioma Grading Using MR
Images,
ICIP18(141-145)
IEEE DOI
1809
Tumors, Magnetic resonance imaging,
Training, Feature extraction, Machine learning, MRIs
BibRef
Murthy, V.,
Hou, L.,
Samaras, D.,
Kurc, T.M.,
Saltz, J.H.,
Center-Focusing Multi-task CNN with Injected Features for
Classification of Glioma Nuclear Images,
WACV17(834-841)
IEEE DOI
1609
Decision support systems, Handheld, computers
BibRef
Swiderska, Z.[Zaneta],
Markiewicz, T.[Tomasz],
Grala, B.[Bartlomiej],
Kozlowski, W.[Wojciech],
Texture and Mathematical Morphology for Hot-Spot Detection in Whole
Slide Images of Meningiomas and Oligodendrogliomas,
CAIP15(II:1-12).
Springer DOI
1511
BibRef
Dvorak, P.[Pavel],
Bartusek, K.[Karel],
Fully Automatic 3D Glioma Extraction in Multi-contrast MRI,
ICIAR14(II: 239-246).
Springer DOI
1410
BibRef
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Brain, Cortex, Dementia .