20.9.1 Brain Tumors, Cortex, Cancer

Ravì, D., Fabelo, H., Callic, G.M., Yang, G.Z.,
Manifold Embedding and Semantic Segmentation for Intraoperative Guidance With Hyperspectral Brain Imaging,
MedImg(36), No. 9, September 2017, pp. 1845-1857.
IEEE DOI 1709
biological tissues, brain, cancer, hyperspectral imaging, image classification, image segmentation, medical image processing, semantic networks, stochastic processes, T-distributed stochastic neighbor approach, brain surgery, dimensionality reduction scheme, hyperspectral brain imaging, semantic segmentation technique, semantic texton forest, tissue classification, tumor classification map, Brain, Cancer, Hyperspectral imaging, Image segmentation, Manifolds, Semantics, Tumors, Manifold embedding, brain cancer detection, hyperspectral imaging, semantic, segmentation BibRef

Anitha, R., Raja, D.S.S.[D. Siva Sundhara],
Segmentation of glioma tumors using convolutional neural networks,
IJIST(27), No. 4, 2017, pp. 354-360.
DOI Link 1712
brain tumors, classifier, features, glioma, image fusion BibRef

Alagarsamy, S.[Saravanan], Kamatchi, K.[Kartheeban], Govindaraj, V.[Vishnuvarthanan], Thiyagarajan, A.P.[Arun Prasath],
A fully automated hybrid methodology using Cuckoo-based fuzzy clustering technique for magnetic resonance brain image segmentation,
IJIST(27), No. 4, 2017, pp. 317-332.
DOI Link 1712
Cuckoo-Based Search, interval type-2 fuzzy clustering, MR brain image segmentation, tumor identification BibRef

Rundo, L.[Leonardo], Militello, C.[Carmelo], Tangherloni, A.[Andrea], Russo, G.[Giorgio], Vitabile, S.[Salvatore], Gilardi, M.C.[Maria Carla], Mauri, G.[Giancarlo],
NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique,
IJIST(28), No. 1, 2018, pp. 21-37.
DOI Link 1802
brain tumors, magnetic resonance imaging, necrosis extraction, neuro-radiosurgery treatments, unsupervised Fuzzy C-Means clustering BibRef

Ding, Y.[Yi], Dong, R.F.[Rong-Feng], Lan, T.[Tian], Li, X.R.[Xue-Rui], Shen, G.Y.[Guang-Yu], Chen, H.[Hao], Qin, Z.[Zhiguang],
Multi-modal brain tumor image segmentation based on SDAE,
IJIST(28), No. 1, 2018, pp. 38-47.
DOI Link 1802
brain tumor segmentation, BRATS 2015, stacked de-noising auto-encoder BibRef

Anitha, R., Raja, D.S.S.[D. Siva Sundhara],
Development of computer-aided approach for brain tumor detection using random forest classifier,
IJIST(28), No. 1, 2018, pp. 48-53.
DOI Link 1802
abnormal patterns, brain tumors, classification, diagnose, segmentation BibRef

Sasikanth, S., Kumar, S.S.[S. Suresh],
Glioma tumor detection in brain MRI image using ANFIS-based normalized graph cut approach,
IJIST(28), No. 1, 2018, pp. 64-71.
DOI Link 1802
classifier, glioma tumor, graph cut approach, orientation analysis, validation BibRef

Wu, G.Q.[Guo-Qing], Chen, Y.S.[Yin-Sheng], Wang, Y.Y.[Yuan-Yuan], Yu, J.H.[Jin-Hua], Lv, X.F.[Xiao-Fei], Ju, X.[Xue], Shi, Z.F.[Zhi-Feng], Chen, L.[Liang], Chen, Z.P.[Zhong-Ping],
Sparse Representation-Based Radiomics for the Diagnosis of Brain Tumors,
MedImg(37), No. 4, April 2018, pp. 893-905.
IEEE DOI 1804
Cancer, Dictionaries, Estimation, Feature extraction, Medical diagnostic imaging, Tumors, Brain tumors, tumor differentiation BibRef

Anantha, N.H.[N. Herald], Selvathi, R.D.[Rufus D.],
Performance analysis of brain tissues and tumor detection and grading system using ANFIS classifier,
IJIST(28), No. 2, 2018, pp. 77-85.
WWW Link. 1806
BibRef

Pinto, A.[Adriano], Pereira, S.[Sérgio], Rasteiro, D.[Deolinda], Silva, C.A.[Carlos A.],
Hierarchical brain tumour segmentation using extremely randomized trees,
PR(82), 2018, pp. 105-117.
Elsevier DOI 1806
Brain tumour, Magnetic resonance imaging, Image segmentation, Hierarchy of classifiers, Extremely randomized trees, Machine learning BibRef

Izadyyazdanabadi, M.[Mohammadhassan], Belykh, E.[Evgenii], Mooney, M.[Michael], Martirosyan, N.[Nikolay], Eschbacher, J.[Jennifer], Nakaji, P.[Peter], Preul, M.C.[Mark C.], Yang, Y.Z.[Ye-Zhou],
Convolutional neural networks: Ensemble modeling, fine-tuning and unsupervised semantic localization for neurosurgical CLE images,
JVCIR(54), 2018, pp. 10-20.
Elsevier DOI 1806
Neural network, Unsupervised localization, Ensemble modeling, Brain tumor, Confocal laser endomicroscopy, Surgical vision BibRef

Arnaud, A., Forbes, F., Coquery, N., Collomb, N., Lemasson, B., Barbier, E.L.,
Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI Data,
MedImg(37), No. 7, July 2018, pp. 1678-1689.
IEEE DOI 1808
biomedical MRI, brain, cancer, feature extraction, Gaussian distribution, image segmentation, fingerprint model BibRef

Ma, C., Luo, G., Wang, K.,
Concatenated and Connected Random Forests With Multiscale Patch Driven Active Contour Model for Automated Brain Tumor Segmentation of MR Images,
MedImg(37), No. 8, August 2018, pp. 1943-1954.
IEEE DOI 1808
Image segmentation, Tumors, Brain modeling, Magnetic resonance imaging, Active contours, Radio frequency, multiscale patch BibRef

Tang, Z., Ahmad, S., Yap, P., Shen, D.,
Multi-Atlas Segmentation of MR Tumor Brain Images Using Low-Rank Based Image Recovery,
MedImg(37), No. 10, October 2018, pp. 2224-2235.
IEEE DOI 1810
Brain, Tumors, Image segmentation, Pathology, Convergence, Radiology, Low-rank, multi-atlas segmentation BibRef

Kermi, A.[Adel], Andjouh, K.[Khaled], Zidane, F.[Ferhat],
Fully automated brain tumour segmentation system in 3D-MRI using symmetry analysis of brain and level sets,
IET-IPR(12), No. 11, November 2018, pp. 1964-1971.
DOI Link 1810
BibRef

Angulakshmi, M., Lakshmi Priya, G.G.,
Walsh Hadamard kernel-based texture feature for multimodal MRI brain tumour segmentation,
IJIST(28), No. 4, December 2018, pp. 254-266.
WWW Link. 1811
BibRef

Selvapandian, A., Manivannan, K.,
Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier,
IJIST(28), No. 4, December 2018, pp. 295-301.
WWW Link. 1811
BibRef

Lian, C., Ruan, S., Denœux, T., Li, H., Vera, P.,
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions,
IP(28), No. 2, February 2019, pp. 755-766.
IEEE DOI 1811
cancer, computerised tomography, image fusion, image segmentation, iterative methods, lung, medical image processing, PET-CT BibRef

Zhou, T., Canu, S., Vera, P., Ruan, S.,
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities,
IP(30), 2021, pp. 4263-4274.
IEEE DOI 2104
Tumors, Image segmentation, Correlation, Magnetic resonance imaging, Brain modeling, Feature extraction, deep learning BibRef

Chen, S.C.[Sheng-Cong], Ding, C.X.[Chang-Xing], Liu, M.F.[Min-Feng],
Dual-force convolutional neural networks for accurate brain tumor segmentation,
PR(88), 2019, pp. 90-100.
Elsevier DOI 1901
Brain tumor segmentation, Dual-force network, Convolutional neural network, Label distribution, Post-processing BibRef

Chang, J.[Jie], Zhang, L.[Luming], Gu, N.[Naijie], Zhang, X.[Xiaoci], Ye, M.[Minquan], Yin, R.[Rongzhang], Meng, Q.Q.[Qian-Qian],
A mix-pooling CNN architecture with FCRF for brain tumor segmentation,
JVCIR(58), 2019, pp. 316-322.
Elsevier DOI 1901
MR image segmentation, Convolutional Neural Network, Fully CRF BibRef

Saman, S.[Sangeetha], Narayanan, S.J.[Swathi Jamjala],
Survey on brain tumor segmentation and feature extraction of MR images,
MultInfoRetr(8), No. 2, June 2019, pp. 79-99.
Springer DOI 1906
Survey, Brain Tumors. BibRef

Rezaei, K.[Kimia], Agahi, H.[Hamed], Mahmoodzadeh, A.[Azar],
Multi-objective differential evolution-based ensemble method for brain tumour diagnosis,
IET-IPR(13), No. 9, 18 July 2019, pp. 1421-1430.
DOI Link 1907
BibRef

Lipková, J., Angelikopoulos, P., Wu, S., Alberts, E., Wiestler, B., Diehl, C., Preibisch, C., Pyka, T., Combs, S.E., Hadjidoukas, P., van Leemput, K., Koumoutsakos, P., Lowengrub, J., Menze, B.,
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans, and Bayesian Inference,
MedImg(38), No. 8, August 2019, pp. 1875-1884.
IEEE DOI 1908
Tumors, Mathematical model, Bayes methods, Biomedical imaging, Magnetic resonance imaging, Predictive models, Glioblastoma, multimodal medical scans BibRef

Rajagopal, R.,
Glioma brain tumor detection and segmentation using weighting random forest classifier with optimized ant colony features,
IJIST(29), No. 3, September 2019, pp. 353-359.
DOI Link 1908
BibRef

Kale, V.V.[Vandana V.], Hamde, S.T.[Satish T.], Holambe, R.S.[Raghunath S.],
Brain disease diagnosis using local binary pattern and steerable pyramid,
MultInfoRetr(8), No. 3, September 2019, pp. 155-165.
Springer DOI 1908
BibRef

Gtifa, W.[Wafa], Hamdaoui, F.[Fayçal], Sakly, A.[Anis],
3D brain tumor segmentation in MRI images based on a modified PSO technique,
IJIST(29), No. 4, 2019, pp. 501-509.
DOI Link 1911
2D images, 3D brain tumor segmentation, modified particle swarm optimization BibRef

Nagarathinam, E.[Ezhilmathi], Ponnuchamy, T.[Thirumurugan],
Image registration-based brain tumor detection and segmentation using ANFIS classification approach,
IJIST(29), No. 4, 2019, pp. 510-517.
DOI Link 1911
abnormal cells, classifications, detection, segmentation, tumor BibRef

Johnpeter, J.H.[Jasmine Hephzipah], Ponnuchamy, T.[Thirumurugan],
Computer aided automated detection and classification of brain tumors using CANFIS classification method,
IJIST(29), No. 4, 2019, pp. 431-438.
DOI Link 1911
abnormal, brain, classification, statistical features, tumors BibRef

Gilanie, G.[Ghulam], Bajwa, U.I.[Usama Ijaz], Waraich, M.M.[Mustansar Mahmood], Habib, Z.[Zulfiqar],
Automated and reliable brain radiology with texture analysis of magnetic resonance imaging and cross datasets validation,
IJIST(29), No. 4, 2019, pp. 531-538.
DOI Link 1911
brain tumor diagnosis, cross dataset validation, MRI texture analysis, neoplastic and non-neoplastic tissues, primary and secondary brain tumor BibRef

Tamilmani, G., Sivakumari, S.,
Early detection of brain cancer using association allotment hierarchical clustering,
IJIST(29), No. 4, 2019, pp. 617-632.
DOI Link 1911
association allotment hierarchical clustering, gray wolf optimization, mutual piece-wise linear transformation filtering BibRef

Meng, H., Wang, K., Gao, Y., Jin, Y., Ma, X., Tian, J.,
Adaptive Gaussian Weighted Laplace Prior Regularization Enables Accurate Morphological Reconstruction in Fluorescence Molecular Tomography,
MedImg(38), No. 12, December 2019, pp. 2726-2734.
IEEE DOI 1912
Fluorescence, Image reconstruction, Imaging, In vivo, Kernel, Probes, Tumors, Fluorescence tomography, multi-modality fusion, brain BibRef

Sharif, M.[Muhammad], Amin, J.[Javaria], Raza, M.[Mudassar], Yasmin, M.[Mussarat], Satapathy, S.C.[Suresh Chandra],
An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor,
PRL(129), 2020, pp. 150-157.
Elsevier DOI 2001
BSE, PSO, GA, LBP, Deep features, ANN BibRef

Amin, J.[Javaria], Sharif, M.[Muhammad], Gul, N.[Nadia], Yasmin, M.[Mussarat], Shad, S.A.[Shafqat Ali],
Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network,
PRL(129), 2020, pp. 115-122.
Elsevier DOI 2001
Sequences, CNN, DWT, Global thresholding, Filter BibRef

Sharif, M.I.[Muhammad Irfan], Li, J.P.[Jian Ping], Khan, M.A.[Muhammad Attique], Saleem, M.A.[Muhammad Asim],
Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images,
PRL(129), 2020, pp. 181-189.
Elsevier DOI 2001
Brain tumor, Contrast improvement, Deep saliency method, Features extraction, Optimization, Recognition BibRef

Mahesh, K.M.[K. Michael], Renjit, J.A.[J. Arokia],
Multiclassifier for severity-level categorization of glioma tumors using multimodal magnetic resonance imaging brain images,
IJIST(30), No. 1, 2020, pp. 234-251.
DOI Link 2002
deep convolutional neural networks, Jaya optimization algorithm, multimodal MRI brain images, severity-level classification BibRef

Mammoli, D., Gordon, J., Autry, A., Larson, P.E.Z., Li, Y., Chen, H., Chung, B., Shin, P., van Criekinge, M., Carvajal, L., Slater, J.B., Bok, R., Crane, J., Xu, D., Chang, S., Vigneron, D.B.,
Kinetic Modeling of Hyperpolarized Carbon-13 Pyruvate Metabolism in the Human Brain,
MedImg(39), No. 2, February 2020, pp. 320-327.
IEEE DOI 2002
Brain cancer, dissolution dynamic nuclear polarization, hyperpolarized MRI, kinetic modeling, kPL, kPB, metabolic imaging BibRef

Hachemi, B.[Belkacem], Chama, Z.[Zouaoui], Alim-Ferhat, F.[Fatiha], Lamini, E.S.[El-Sedik], Abderrahmane, A.[Abdelkader], Anani, M.[Macho], Choquet, C.[Catherine],
Fully automatic multisegmentation approach for magnetic resonance imaging brain tumor detection using improved region-growing and quasi-Monte Carlo-expectation maximization algorithm,
IJIST(30), No. 1, 2020, pp. 104-111.
DOI Link 2002
brain tumor, expectation maximization, multisegmentation, quasi-Monte Carlo, region growing BibRef

Peng, S.T.[Su-Ting], Chen, W.[Wei], Sun, J.W.[Jia-Wei], Liu, B.Q.[Bo-Qiang],
Multi-Scale 3D U-Nets: An approach to automatic segmentation of brain tumor,
IJIST(30), No. 1, 2020, pp. 5-17.
DOI Link 2002
brain tumor, CNN, deep learning, gliomas, image segmentation BibRef

Zhou, C., Ding, C., Wang, X., Lu, Z., Tao, D.,
One-Pass Multi-Task Networks With Cross-Task Guided Attention for Brain Tumor Segmentation,
IP(29), 2020, pp. 4516-4529.
IEEE DOI 2003
Tumors, Task analysis, Image segmentation, Brain modeling, Computational modeling, Training, Magnetic resonance imaging, channel attention BibRef

Nasor, M.[Mohamed], Obaid, W.[Walid],
Detection and localisation of multiple brain tumours by object counting and elimination,
IET-IPR(14), No. 4, 27 March 2020, pp. 615-620.
DOI Link 2003
BibRef

Goceri, E.[Evgin],
CapsNet topology to classify tumours from brain images and comparative evaluation,
IET-IPR(14), No. 5, 17 April 2020, pp. 882-889.
DOI Link 2004
BibRef

Tiwari, A.[Arti], Srivastava, S.[Shilpa], Pant, M.[Millie],
Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019,
PRL(131), 2020, pp. 244-260.
Elsevier DOI 2004
Brain tumor segmentation, Glioma, Neoplasia, Magnetic resonance imaging (MRI) BibRef

Zhang, W.X.[Wen-Xue], Jian, J.B.[Jian-Bo], Sun, C.[Cuiyun], Chen, J.[Jie], Lv, W.J.[Wen-Juan], Sun, M.Y.[Meng-Yu], Zhao, Y.Q.[Yu-Qing], Zhao, Q.[Qi], Hu, C.H.[Chun-Hong],
High-resolution 3D imaging of microvascular architecture in human glioma tissues using X-ray phase-contrast computed tomography as a potential adjunct to histopathology,
IJIST(30), No. 2, 2020, pp. 464-472.
DOI Link 2005
glioma, microthrombi, microvascular architecture, X-ray phase-contrast computed tomography BibRef

Lu, Z.Y.[Zhen-Yu], Bai, Y.Z.[Yan-Zhong], Chen, Y.[Yi], Su, C.Q.[Chun-Qiu], Lu, S.S.[Shan-Shan], Zhan, T.M.[Tian-Ming], Hong, X.N.[Xun-Ning], Wang, S.H.[Shui-Hua],
The classification of gliomas based on a Pyramid dilated convolution resnet model,
PRL(133), 2020, pp. 173-179.
Elsevier DOI 2005
Gliomas, Classification, Deep learning, ResNet, Dilated convolution BibRef

Dandil, E.[Emre], Biçer, A.[Ali],
Automatic grading of brain tumours using LSTM neural networks on magnetic resonance spectroscopy signals,
IET-IPR(14), No. 10, August 2020, pp. 1967-1979.
DOI Link 2008
BibRef

Chithra, P.L., Dheepa, G.,
Di-phase midway convolution and deconvolution network for brain tumor segmentation in MRI images,
IJIST(30), No. 3, 2020, pp. 674-686.
DOI Link 2008
brain tumor segmentation, di-phase midway convolution and deconvolution network, upsampling BibRef

Zhang, D., Huang, G., Zhang, Q., Han, J., Han, J., Wang, Y., Yu, Y.,
Exploring Task Structure for Brain Tumor Segmentation From Multi-Modality MR Images,
IP(29), 2020, pp. 9032-9043.
IEEE DOI 2009
Tumors, Task analysis, Image segmentation, Brain modeling, supervised learning BibRef

Kurmi, Y.[Yashwant], Chaurasia, V.[Vijayshri],
Classification of magnetic resonance images for brain tumour detection,
IET-IPR(14), No. 12, October 2020, pp. 2808-2818.
DOI Link 2010
BibRef

Mano, A.[Abhisha], Anand, S.[Swaminathan],
Method of multi-region tumour segmentation in brain MRI images using grid-based segmentation and weighted bee swarm optimisation,
IET-IPR(14), No. 12, October 2020, pp. 2901-2910.
DOI Link 2010
BibRef

Liu, X.M.[Xiao-Ming], Zhou, X.B.[Xiao-Bo], Qian, X.H.[Xiao-Hua],
Transparency-guided ensemble convolutional neural network for the stratification between pseudoprogression and true progression of glioblastoma multiform in MRI,
JVCIR(72), 2020, pp. 102880.
Elsevier DOI 2010
Pseudo progression, Glioblastoma multiforme, Diffusion tensor imaging (DTI), Ensemble CNN BibRef

Amin, J.[Javeria], Sharif, M.[Muhammad], Yasmin, M.[Mussarat], Fernandes, S.L.[Steven Lawrence],
A distinctive approach in brain tumor detection and classification using MRI,
PRL(139), 2020, pp. 118-127.
Elsevier DOI 2011
Cells, Tumors, Segmentation, Lesion, Tissues BibRef

Zhang, D.W.[Ding-Wen], Huang, G.H.[Guo-Hai], Zhang, Q.A.[Qi-Ang], Han, J.G.[Jun-Gong], Han, J.W.[Jun-Wei], Yu, Y.Z.[Yi-Zhou],
Cross-modality deep feature learning for brain tumor segmentation,
PR(110), 2021, pp. 107562.
Elsevier DOI 2011
Brain tumor segmentation, Cross-modality feature transition, Cross-modality feature fusion, Feature learning BibRef

Yepuganti, K.[Karuna], Saladi, S.[Saritha], Narasimhulu, C.V.,
Segmentation of tumor using PCA based modified fuzzy C means algorithms on MR brain images,
IJIST(30), No. 4, 2020, pp. 1337-1345.
DOI Link 2011
brain tumor, DWT, feature extraction, fuzzy C means and MRI BibRef

Leena, B.[Bojaraj], Jayanthi, A.[Annamalai],
Brain tumor segmentation and classification via adaptive CLFAHE with hybrid classification,
IJIST(30), No. 4, 2020, pp. 874-898.
DOI Link 2011
brain tumor classification, feature extraction, optimization, segmentation, skull stripping BibRef

Kalaiselvi, T.[Thiruvenkadam], Padmapriya, T.[Thiyagarajan], Sriramakrishnan, P.[Padmanaban], Priyadharshini, V.[Venugopal],
Development of automatic glioma brain tumor detection system using deep convolutional neural networks,
IJIST(30), No. 4, 2020, pp. 926-938.
DOI Link 2011
BraTS, deep learning, glioma tumor, neural networks, tumor detection, WBA BibRef

Afshar, P., Mohammadi, A., Plataniotis, K.N.,
BayesCap: A Bayesian Approach to Brain Tumor Classification Using Capsule Networks,
SPLetters(27), 2020, pp. 2024-2028.
IEEE DOI 2012
BibRef
Earlier: A1, A3, A2:
Capsule Networks' Interpretability for Brain Tumor Classification Via Radiomics Analyses,
ICIP19(3816-3820)
IEEE DOI 1910
BibRef
And: A1, A2, A3:
Brain Tumor Type Classification via Capsule Networks,
ICIP18(3129-3133)
IEEE DOI 1809
Bayes methods, Uncertainty, Tumors, Predictive models, Deep learning, Measurement uncertainty, Brain modeling, Radiomics. Brain Tumor Classification, Capsule Networks, Explainability. Feature extraction, Magnetic resonance imaging, Neurons, Computer architecture, Cancer, Training data, Convolutional neural networks BibRef

Afshar, P., Shahroudnejad, A., Mohammadi, A., Plataniotis, K.N.,
CARISI: Convolutional Autoencoder-Based Inter-Slice Interpolation of Brain Tumor Volumetric Images,
ICIP18(1458-1462)
IEEE DOI 1809
Tumors, Interpolation, Image reconstruction, Shape, Computed tomography, Convolution, Convolutional auto-encoder BibRef

Sran, P.K.[Paramveer Kaur], Gupta, S.[Savita], Singh, S.[Sukhwinder],
Integrating saliency with fuzzy thresholding for brain tumor extraction in MR images,
JVCIR(74), 2021, pp. 102964.
Elsevier DOI 2101
Saliency, Fuzzy, Segmentation, ROI, Medical Images BibRef

Ragupathy, B.[Balakumaresan], Karunakaran, M.[Manivannan],
A deep learning model integrating convolution neural network and multiple kernel K means clustering for segmenting brain tumor in magnetic resonance images,
IJIST(31), No. 1, 2021, pp. 118-127.
DOI Link 2102
convolutional neural network, magnetic resonance image, multi kernel K means clustering, segmentation, tumor detection BibRef

Thiruvenkadam, K.[Kalaiselvi], Nagarajan, K.[Kalaichelvi],
Fully automatic brain tumor extraction and tissue segmentation from multimodal MRI brain images,
IJIST(31), No. 1, 2021, pp. 336-350.
DOI Link 2102
3D volume estimation, bit plane slicing, brain tumor, extended maxima, FCM, inverse log transform, MRI BibRef

Hu, J.Y.[Jing-Yu], Gu, X.J.[Xiao-Jing], Gu, X.S.[Xing-Sheng],
Dual-pathway DenseNets with fully lateral connections for multimodal brain tumor segmentation,
IJIST(31), No. 1, 2021, pp. 364-378.
DOI Link 2102
brain tumor, convolutional network, dense network, multimodalities, segmentation BibRef

Ragupathy, B.[Balakumaresan], Karunakaran, M.[Manivannan],
A fuzzy logic-based meningioma tumor detection in magnetic resonance brain images using CANFIS and U-Net CNN classification,
IJIST(31), No. 1, 2021, pp. 379-390.
DOI Link 2102
brain, classifications, features, meningioma, tumor BibRef

Mohamed, L.A.[Linda Ait], Cherfa, A.[Assia], Cherfa, Y.[Yazid], Belkhamsa, N.[Noureddine], Alim-Ferhat, F.[Fatiha],
Hybrid method combining superpixel, supervised learning, and random walk for glioma segmentation,
IJIST(31), No. 1, 2021, pp. 288-301.
DOI Link 2102
glioma tumor, random forest, random walk, segmentation, superpixels BibRef

Singh, R.[Rahul], Goel, A.[Aditya], Raghuvanshi, D.K.,
Ensemble-based glioma grade classification using Gabor filter bank and rotation forest,
IET-IPR(14), No. 15, 15 December 2020, pp. 3851-3858.
DOI Link 2103
BibRef

Shirali, A.[Armaghan], Shiri, N.[Nabiollah],
Diagnosis of brain tumours by MRI binarisation with variable fuzzy level,
IET-IPR(14), No. 16, 19 December 2020, pp. 4269-4276.
DOI Link 2103
BibRef

Kamli, A.[Adel], Saouli, R.[Rachida], Batatia, H.[Hadj], Ben Naceur, M.[Mostefa], Youkana, I.[Imane],
Synthetic medical image generator for data augmentation and anonymisation based on generative adversarial network for glioblastoma tumors growth prediction,
IET-IPR(14), No. 16, 19 December 2020, pp. 4248-4257.
DOI Link 2103
BibRef

Yu, B.T.[Bi-Ting], Zhou, L.P.[Lu-Ping], Wang, L.[Lei], Yang, W.Q.[Wan-Qi], Yang, M.[Ming], Bourgeat, P.[Pierrick], Fripp, J.[Jurgen],
SA-LuT-Nets: Learning Sample-Adaptive Intensity Lookup Tables for Brain Tumor Segmentation,
MedImg(40), No. 5, May 2021, pp. 1417-1427.
IEEE DOI 2105
Image segmentation, Table lookup, Tumors, Task analysis, Solid modeling, neural network BibRef

Jeevanantham, V., MohanBabu, G.,
Detection and diagnosis of brain tumors-framework using extreme machine learning and CANFIS classification algorithms,
IJIST(31), No. 2, 2021, pp. 540-547.
DOI Link 2105
brain, features, machine learning, transforms, tumors BibRef

Kaushal, B.[Bhakti], Patil, M.D.[Mukesh D.], Birajdar, G.K.[Gajanan K.],
Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging,
IJIST(31), No. 2, 2021, pp. 575-591.
DOI Link 2105
brain tumor detection, discrete wavelet transform, fractional Fourier transform, fractional wavelet transform BibRef

Hu, A.[An], Razmjooy, N.[Navid],
Brain tumor diagnosis based on metaheuristics and deep learning,
IJIST(31), No. 2, 2021, pp. 657-669.
DOI Link 2105
brain tumor, deep belief network, feature extraction, feature selection, classification, improved seagull optimization algorithm BibRef

Dzulkifli, F.A.[Fahmi Akmal], Mashor, M.Y.[Mohd Yusoff], Jaafar, H.[Hasnan],
Colour thresholding-based automatic Ki67 counting procedure for Immunohistochemical staining in meningioma,
IJCVR(11), No. 3, 2021, pp. 279-298.
DOI Link 2106
BibRef

Lerche, C.W.[Christoph W.], Radomski, T.[Timon], 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 BibRef

Bagyaraj, S.[Sanjeevirayar], Tamilselvi, R.[Rajendran], Gani, P.B.M.[Parisa Beham Mohamed], Sabarinathan, D.[Devanathan],
Brain tumour cell segmentation and detection using deep learning networks,
IET-IPR(15), No. 10, 2021, pp. 2363-2371.
DOI Link 2108
BibRef

Alaraimi, S.[Saleh], Okedu, K.E.[Kenneth E.], Tianfield, H.[Hugo], Holden, R.[Richard], Uthmani, O.[Omair],
Transfer learning networks with skip connections for classification of brain tumors,
IJIST(31), No. 3, 2021, pp. 1564-1582.
DOI Link 2108
AlexNet, convolutional neural network (CNN), deep learning, GoogLeNet, transfer learning, VGG BibRef

Deepak, S., Ameer, P.M.,
Brain tumour classification using siamese neural network and neighbourhood analysis in embedded feature space,
IJIST(31), No. 3, 2021, pp. 1655-1669.
DOI Link 2108
brain tumour, classification, Mahalanobis distance, neighbourhood, Siamese networks BibRef

Kakarla, J.[Jagadeesh], Isunuri, B.V.[Bala Venkateswarlu], Doppalapudi, K.S.[Krishna Sai], Bylapudi, K.S.R.[Karthik Satya Raghuram],
Three-class classification of brain magnetic resonance images using average-pooling convolutional neural network,
IJIST(31), No. 3, 2021, pp. 1731-1740.
DOI Link 2108
average pooling, brain tumor classification, brain tumor dataset, convolutional neural network, three-class classification BibRef

Rafi, A.[Asra], Madni, T.M.[Tahir Mustafa], Janjua, U.I.[Uzair Iqbal], Ali, M.J.[Muhammad Junaid], Abid, M.N.[Muhammad Naeem],
Multi-level dilated convolutional neural network for brain tumour segmentation and multi-view-based radiomics for overall survival prediction,
IJIST(31), No. 3, 2021, pp. 1519-1535.
DOI Link 2108
brain disease, brain tumour segmentation, dilated convolution, magnetic resonance imaging, multi-view, overall survival, random forest BibRef

Mathiyalagan, G.[Gomathi], Devaraj, D.[Dhanasekaran],
A machine learning classification approach based glioma brain tumor detection,
IJIST(31), No. 3, 2021, pp. 1424-1436.
DOI Link 2108
features, fuzzy logic, glioma, ridgelet filter, tumor BibRef

Gurunathan, A.[Akila], Krishnan, B.[Batri],
Detection and diagnosis of brain tumors using deep learning convolutional neural networks,
IJIST(31), No. 3, 2021, pp. 1174-1184.
DOI Link 2108
brain, deep learning, machine learning, segmentation, tumors BibRef

Arumugam, S.[Selvapandian], Paulraj, S.[Sivakumar], Selvaraj, N.P.[Nagendra Prabhu],
Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique,
IJIST(31), No. 3, 2021, pp. 1185-1196.
DOI Link 2108
binary cuckoo search, magnetic resonance imaging, neural network, neuro fuzzy with binary cuckoo search, singular value decomposition BibRef

Barzegar, Z.[Zeynab], Jamzad, M.[Mansour],
Fully automated glioma tumour segmentation using anatomical symmetry plane detection in multimodal brain MRI,
IET-CV(15), No. 7, 2021, pp. 463-473.
DOI Link 2109
BibRef

Anantharajan, S.[Shenbagarajan], Gunasekaran, S.[Shenbagalakshmi],
Automated brain tumor detection and classification using weighted fuzzy clustering algorithm, deep auto encoder with barnacle mating algorithm and random forest classifier techniques,
IJIST(31), No. 4, 2021, pp. 1970-1988.
DOI Link 2112
barnacle mating algorithm, deep auto-encoder, magnetic resonance imaging, random forest classifier, weighted fuzzy clustering algorithm BibRef

Abdelaziz, M.[Mohammed], Cherfa, Y.[Yazid], Cherfa, A.[Assia], Alim-Ferhat, F.[Fatiha],
Automatic brain tumor segmentation for a computer-aided diagnosis system,
IJIST(31), No. 4, 2021, pp. 2226-2236.
DOI Link 2112
3D reconstruction, Graph Cut, Level Set, Random Forest, segmentation BibRef

Liu, T.T.[Ting-Ting], Yuan, Z.[Zhi], Wu, L.[Li], Badami, B.[Benjamin],
Optimal brain tumor diagnosis based on deep learning and balanced sparrow search algorithm,
IJIST(31), No. 4, 2021, pp. 1921-1935.
DOI Link 2112
balanced sparrow search algorithm, brain tumor diagnosis, discrete wavelet transform, gray-level co-occurrence matrix BibRef

Zhang, W.[Wenbo], Yang, G.[Guang], Huang, H.[He], Yang, W.J.[Wei-Ji], Xu, X.M.[Xiao-Mei], Liu, Y.K.[Yong-Kai], Lai, X.B.[Xiao-Bo],
ME-Net: Multi-encoder net framework for brain tumor segmentation,
IJIST(31), No. 4, 2021, pp. 1834-1848.
DOI Link 2112
automatic segmentation, brain tumor segmentation, deep learning, magnetic resonance imaging, multi-encoder net BibRef

Latif, U.[Urva], Shahid, A.R.[Ahmad R.], Raza, B.[Basit], Ziauddin, S.[Sheikh], Khan, M.A.[Muazzam A.],
An end-to-end brain tumor segmentation system using multi-inception-UNET,
IJIST(31), No. 4, 2021, pp. 1803-1816.
DOI Link 2112
brain tumor, BRATS, CNN, inception, UNET BibRef

Singh, R.[Rahul], Goel, A.[Aditya], Raghuvanshi, D.K.[Deepak Kumar],
Binary glioma grading framework employing locality preserving projections and Gaussian radial basis function support vector machine,
IJIST(31), No. 4, 2021, pp. 2047-2059.
DOI Link 2112
Gabor filter bank, Gaussian radial basis function-support vector machine (GRBF-SVM), synthetic minority over-sampling technique (SMOTE) BibRef

Tunga, P.P.[P. Prakash], Singh, V.[Vipula], Aditya, V.S.[V. Sri], Subramanya, N.,
U-Net Model-Based Classification and Description of Brain Tumor in MRI Images,
IJIG(21), No. 5 2021, pp. 2140005.
DOI Link 2201
BibRef

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 segmentation,
SP:IC(101), 2022, pp. 116553.
Elsevier DOI 2201
Brain tumor segmentation, U-Net, Full convolutional network, Inception, Residual Module, Attention Gate BibRef

Siar, M.[Masoumeh], Teshnehlab, M.[Mohammad],
A combination of feature extraction methods and deep learning for brain tumour classification,
IET-IPR(16), No. 2, 2022, pp. 416-441.
DOI Link 2201
BibRef

Adu, K.[Kwabena], Yu, Y.B.[Yong-Bin], Cai, J.Y.[Jing-Ye], Asare, I.[Isaac], Quahin, J.[Jennifer],
The influence of the activation function in a capsule network for brain tumor type classification,
IJIST(32), No. 1, 2022, pp. 123-143.
DOI Link 2201
activation function, brain tumor classification, capsule network, convolutional neural network, deep learning BibRef

Asthana, P.[Pallavi], Hanmandlu, M.[Madasu], Vashisth, S.[Sharda],
Classification of brain tumor from magnetic resonance images using probabilistic features and possibilistic Hanman-Shannon transform classifier,
IJIST(32), No. 1, 2022, pp. 280-294.
DOI Link 2201
brain tumor, classification, feature extraction, Hanman-Shannon transform classifier, probabilistic features BibRef

Anjum, S.[Sadia], Hussain, L.[Lal], Ali, M.[Mushtaq], Alkinani, M.H.[Monagi H.], Aziz, W.[Wajid], Gheller, S.[Sabrina], Abbasi, A.A.[Adeel Ahmed], Marchal, A.R.[Ali Raza], Suresh, H.[Harshini], Duong, T.Q.[Tim Q.],
Detecting brain tumors using deep learning convolutional neural network with transfer learning approach,
IJIST(32), No. 1, 2022, pp. 307-323.
DOI Link 2201
brain tumor, convolution neural network, decision tree, deep learning 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, L.L.[Ling-Ling], Wang, X.[Xin],
Brain tumor segmentation based on the dual-path network of multi-modal MRI images,
PR(124), 2022, pp. 108434.
Elsevier DOI 2203
Brain tumor segmentation, Deep learning, Dual-path model, Magnetic resonance imaging, Multi-modal images BibRef

Raghavendra, U., Gudigar, A.[Anjan], Rao, T.N.[Tejaswi N.], Rajinikanth, V., Ciaccio, E.J.[Edward J.], Yeong, C.H.[Chai Hong], Satapathy, S.C.[Suresh Chandra], Molinari, F.[Filippo], Acharya, U.R.[U. Rajendra],
Feature-versus deep learning-based approaches for the automated detection of brain tumor with magnetic resonance images: A comparative study,
IJIST(32), No. 2, 2022, pp. 501-516.
DOI Link 2203
brain tumor, classification, deep learning, elongated quinary patterns, glioblastoma, texture features BibRef

Shanmugam, S.[Sasikanth], Surampudi, S.R.[Srinivasa Rao],
A method for detecting and classifying the tumor regions in brain MRI images using vector index filtering and ANFIS classification process,
IJIST(32), No. 2, 2022, pp. 687-696.
DOI Link 2203
ANFIS, BRATS, fast wavelet transform, magnetic resonance imaging, vector index filtering BibRef

Fang, Y.[Ying], Huang, H.[He], Yang, W.J.[Wei-Ji], Xu, X.[Xiaomei], 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

Sun, M.L.[Meng-Li], Zou, W.[Wei], Hu, N.[Nan], Wang, J.J.[Jia-Jun], Chi, Z.[Zheru],
Iterative brain tumor retrieval for MR images based on user's intention model,
PR(127), 2022, pp. 108650.
Elsevier DOI 2205
CBIR, Brain tumor images, Eye-tracking, Intention similarity, Iterative retrieval, Relevance feedback BibRef

Alpar, O.[Orcan], Dolezal, R.[Rafael], Ryska, P.[Pavel], Krejcar, O.[Ondrej],
Nakagami-Fuzzy imaging framework for precise lesion segmentation in MRI,
PR(128), 2022, pp. 108675.
Elsevier DOI 2205
Nakagami imaging, Fuzzy c-means, Lesion segmentation, MRI, BraTS BibRef

Fiaz, K.[Kiran], Madni, T.M.[Tahir Mustafa], Anwar, F.[Fozia], Janjua, U.I.[Uzair Iqbal], Rafi, A.[Asra], Abid, M.M.N.[Mian Muhammad Naeem], Sultana, N.[Nasira],
Brain tumor segmentation and multiview multiscale-based radiomic model for patient's overall survival prediction,
IJIST(32), No. 3, 2022, pp. 982-999.
DOI Link 2205
brain tumor segmentation, glioblastoma, MRI, radiomic feature extraction, survival prediction BibRef

Liu, F.[Fan], Li, D.X.[Dong-Xiao], Jin, X.Y.[Xin-Yu], Qiu, W.Y.[Wen-Yuan],
Accelerated brain tumor dynamic contrast-enhanced MRI using Adaptive Pharmaco-Kinetic Model Constrained method,
IJIST(32), No. 3, 2022, pp. 728-739.
DOI Link 2205
accelerated DCE-MRI, adaptive dictionary, brain tumor, compressed sensing, Pharmacokinetic modeling BibRef

Cui, S.[Shaoguo], Wei, M.[Mingjun], Liu, C.[Chang], Jiang, J.F.[Jing-Feng],
GAN-segNet: A deep generative adversarial segmentation network for brain tumor semantic segmentation,
IJIST(32), No. 3, 2022, pp. 857-868.
DOI Link 2205
autoencoder, brain tumor, generative adversarial network, label imbalance, semantic segmentation BibRef

Ezhov, I.[Ivan], Mot, T.[Tudor], Shit, S.[Suprosanna], Lipkova, J.[Jana], Paetzold, J.C.[Johannes C.], Kofler, F.[Florian], Pellegrini, C.[Chantal], Kollovieh, M.[Marcel], Navarro, F.[Fernando], Li, H.W.[Hong-Wei], Metz, M.[Marie], Wiestler, B.[Benedikt], Menze, B.[Bjoern],
Geometry-Aware Neural Solver for Fast Bayesian Calibration of Brain Tumor Models,
MedImg(41), No. 5, May 2022, pp. 1269-1278.
IEEE DOI 2205
Tumors, Numerical models, Computational modeling, Brain modeling, Mathematical models, Biological system modeling, Bayes methods, FET-PET 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

Shivaprasad, B.J., Ravikumar, M., Guru, D.S.,
Analysis of Brain Tumor Using MR Images: A Brief Survey,
IJIG(22), No. 2, April 2022, pp. 2250023.
DOI Link 2205
BibRef

Zhou, T.X.[Tong-Xue], Vera, P.[Pierre], Canu, S.[Stéphane], Ruan, S.[Su],
Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation,
PRL(158), 2022, pp. 125-132.
Elsevier DOI 2205
Brain tumor segmentation, Conditional generator, Correlation learning, Missing data, Multimodal fusion 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

Ismail, M.[Marwa], Prasanna, P.[Prateek], Bera, K.[Kaustav], Statsevych, V.[Volodymyr], Hill, V.[Virginia], Singh, G.[Gagandeep], Partovi, S.[Sasan], Beig, N.[Niha], McGarry, S.[Sean], Laviolette, P.[Peter], Ahluwalia, M.[Manmeet], Madabhushi, A.[Anant], Tiwari, P.[Pallavi],
Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to Characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma,
MedImg(41), No. 7, July 2022, pp. 1764-1777.
IEEE DOI 2207
Tumors, Strain, Feature extraction, Training, Radiomics, Magnetic resonance imaging, Cancer, Glioblastoma, survival, LASSO 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

Ding, Y.H.[Yu-Hang], Yu, X.[Xin], Yang, Y.[Yi],
RFNet: Region-aware Fusion Network for Incomplete Multi-modal Brain Tumor Segmentation,
ICCV21(3955-3964)
IEEE DOI 2203
Training, Degradation, Image segmentation, Sensitivity, Magnetic resonance imaging, Aggregates, Medical, biological, BibRef

Sagar, A.[Abhinav],
Uncertainty Quantification using Variational Inference for Biomedical Image Segmentation,
VAQuality22(44-51)
IEEE DOI 2202
Weight measurement, Image segmentation, Uncertainty, Brain modeling, Time measurement, Decoding, Bayes methods BibRef

Abolvardi, A.A.[Ava Assadi], Hamey, L.[Len], Ho-Shon, K.[Kevin],
UNET-Based Multi-Task Architecture for Brain Lesion Segmentation,
DICTA20(1-7)
IEEE DOI 2201
Deep learning, Training, Image segmentation, Lesions, Task analysis, Biomedical imaging, Deep Learning, Multi-task learning 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

Nguyen, T.H.[Thanh Hau], Le, C.H.[Cong Hau], Sang, D.V.[Dinh Viet], Yao, T.T.[Ting-Ting], Li, W.[Wei], Wang, Z.Y.[Zhi-Yong],
Efficient Brain Tumor Segmentation with Dilated Multi-fiber Network and Weighted Bi-directional Feature Pyramid Network,
DICTA20(1-7)
IEEE DOI 2201
Deep learning, Computer architecture, Bidirectional control, Network architecture, Tumors, Cancer, Software development management BibRef

Cirillo, M.D.[Marco Domenico], Abramian, D.[David], Eklund, A.[Anders],
What is the Best Data Augmentation for 3D Brain Tumor Segmentation?,
ICIP21(36-40)
IEEE DOI 2201
Training, Image segmentation, Brightness, Standards, Tumors, Data augmentation, 3D brain tumor segmentation, MRI, 3D U-Net, artificial intelligence BibRef

Le, N.[Ngan], Yamazaki, K.[Kashu], Quach, K.G.[Kha Gia], Truong, D.[Dat], Savvides, M.[Marios],
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection Segmentation,
ICPR21(5943-5950)
IEEE DOI 2105
Measurement, Image segmentation, Convolution, Brain modeling, Proposals, Kernel BibRef

Ji, Z., Han, X., Lin, T., Wang, W.,
A Dense-Gated U-Net for Brain Lesion Segmentation,
VCIP20(104-107)
IEEE DOI 2102
biomedical MRI, brain, diseases, feature extraction, image segmentation, medical image processing, tumours, dense gates BibRef

Nwe, T.L., Min, O.Z., Gopalakrishnan, S., Lin, D., Prasad, S., Dong, S., Li, Y., Pahwa, R.S.,
Improving 3D Brain Tumor Segmentation With Predict-Refine Mechanism Using Saliency And Feature Maps,
ICIP20(2671-2675)
IEEE DOI 2011
Tumors, Image segmentation, Magnetic resonance imaging, Training, Brain modeling, Brain tumor Segmentation BibRef

El Kaitouni, S.E.I., Tairi, H.,
Segmentation of medical images for the extraction of brain tumors: A comparative study between the Hidden Markov and Deep Learning approaches,
ISCV20(1-5)
IEEE DOI 2011
biomedical MRI, brain, feature extraction, image classification, image segmentation, learning (artificial intelligence), Medical images. BibRef

Kong, L.W.[Ling-Wei], Zhang, Y.F.[Yi-Feng],
Multi-modal Brain Tumor Segmentation Using Cascaded 3D U-Net,
ICIVC21(129-133)
IEEE DOI 2112
Training, Degradation, Image segmentation, Magnetic resonance imaging, weighted Tversky loss BibRef

Li, K., Kong, L.W.[Ling-Wei], Zhang, Y.F.[Yi-Feng],
3D U-Net Brain Tumor Segmentation Using VAE Skip Connection,
ICIVC20(97-101)
IEEE DOI 2009
Image segmentation, Tumors, Decoding, Magnetic resonance imaging, Semantics, Computer architecture, ShakeDrop BibRef

Xi, N.,
Semi-supervised Attentive Mutual-info Generative Adversarial Network for Brain Tumor Segmentation,
IVCNZ19(1-7)
IEEE DOI 2004
biomedical MRI, brain, image segmentation, mutual information, learning (artificial intelligence), medical image processing BibRef

Liu, S.[Sun'ao], Xu, H.[Hai], Liu, Y.Z.[Yi-Zhi], Xie, H.T.[Hong-Tao],
Improving Brain Tumor Segmentation with Dilated Pseudo-3d Convolution and Multi-direction Fusion,
MMMod20(I:727-738).
Springer DOI 2003
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Jia, Z.D.[Zhong-Dao], Yuan, Z.M.[Zhi-Min], Peng, J.L.[Jia-Lin],
Multimodal Brain Tumor Segmentation Using Encoder-decoder with Hierarchical Separable Convolution,
MBIA19(130-138).
Springer DOI 1912
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Liu, H.Y.[Hong-Ying], Shen, X.J.[Xiong-Jie], Shang, F.H.[Fan-Hua], Ge, F.H.[Fei-Hang], Wang, F.[Fei],
Cu-net: Cascaded U-net with Loss Weighted Sampling for Brain Tumor Segmentation,
MBIA19(102-111).
Springer DOI 1912
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Nalepa, J., Mrukwa, G., Piechaczek, S., Lorenzo, P.R., Marcinkiewicz, M., Bobek-Billewicz, B., Wawrzyniak, P., Ulrych, P., Szymanek, J., Cwiek, M., Dudzik, W., Kawulok, M., Hayball, M.P.,
Data Augmentation via Image Registration,
ICIP19(4250-4254)
IEEE DOI 1910
Deep learning, data augmentation, image registration, brain-tumor segmentation BibRef

Sun, Y., Zhou, C., Fu, Y., Xue, X.,
Parasitic GAN for Semi-Supervised Brain Tumor Segmentation,
ICIP19(1535-1539)
IEEE DOI 1910
Generative adversarial networks, medical image processing, volume segmentation BibRef

Zhao, H., Guo, Y., Zheng, Y.,
A Compound Neural Network for Brain Tumor Segmentation,
ICIP19(1435-1439)
IEEE DOI 1910
Convolutional Neural Network, Brain Tumor Segmentation, Magnetic Resonance Imaging, Automated System, Features Extracting 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
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Talamonti, C.[Cinzia], Piffer, S.[Stefano], Greto, D.[Daniela], Mangoni, M.[Monica], Ciccarone, A.[Antonio], Dicarolo, P.[Paolo], Fantacci, M.E.[Maria Evelina], Fusi, F.[Franco], Oliva, P.[Piernicola], Palumbo, L.[Letizia], Favre, C.[Claudio], Livi, L.[Lorenzo], Pallotta, S.[Stefania], Retico, A.[Alessandra],
Radiomic and Dosiomic Profiling of Paediatric Medulloblastoma Tumours Treated with Intensity Modulated Radiation Therapy,
CAIPWS19(56-64).
Springer DOI 1909
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Abd-Ellah, M.K.[Mahmoud Khaled], Khalaf, A.A.M.[Ashraf A. M.], Awad, A.I.[Ali Ismail], Hamed, H.F.A.[Hesham F. A.],
TPUAR-Net: Two Parallel U-Net with Asymmetric Residual-Based Deep Convolutional Neural Network for Brain Tumor Segmentation,
ICIAR19(II:106-116).
Springer DOI 1909
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Cui, S.[Siming], Shen, X.[Xuanjing], Lyu, Y.[Yingda],
Automatic Segmentation of Brain Tumor Image Based on Region Growing with Co-constraint,
MMMod19(I:603-615).
Springer DOI 1901
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Dhara, A.K., Arvids, E., Fahlström, M., Wikström, J., Larsson, E., Strand, R.,
Interactive Segmentation of Glioblastoma for Post-surgical Treatment Follow-up,
ICPR18(1199-1204)
IEEE DOI 1812
Image segmentation, Tumors, Magnetic resonance imaging, Training, Surgery, Tools, Convolution BibRef

Chen, X.[Xuan], Liew, J.H.[Jun Hao], Xiong, W.[Wei], Chui, C.K.[Chee-Kong], Ong, S.H.[Sim-Heng],
Focus, Segment and Erase: An Efficient Network for Multi-label Brain Tumor Segmentation,
ECCV18(XIII: 674-689).
Springer DOI 1810
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Zhang, L.[Lichi], Zhang, H.[Han], Rekik, I.[Islem], Gao, Y.Z.[Yao-Zong], Wang, Q.[Qian], Shen, D.G.[Ding-Gang],
Malignant Brain Tumor Classification Using the Random Forest Method,
SSSPR18(14-21).
Springer DOI 1810
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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
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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

Bousselham, A., Bouattane, O., Youssfi, M., Raihani, A.,
Thermal effect analysis of brain tumor on simulated T1-weighted MRI images,
ISCV18(1-6)
IEEE DOI 1807
biomedical MRI, biothermics, brain, finite difference methods, medical image processing, spin-lattice relaxation, finite difference method BibRef

Shen, H., Zhang, J., Zheng, W.,
Efficient symmetry-driven fully convolutional network for multimodal brain tumor segmentation,
ICIP17(3864-3868)
IEEE DOI 1803
Convolutional codes, Image segmentation, Task analysis, Training, Tumors, brain tumor segmentation BibRef

Shen, H., Zhang, J.,
Fully connected CRF with data-driven prior for multi-class brain tumor segmentation,
ICIP17(1727-1731)
IEEE DOI 1803
biomedical MRI, brain, image segmentation, learning (artificial intelligence), medical image processing, prior 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

Urien, H.[Hélène], Buvat, I.[Irène], Rougon, N.[Nicolas], Soussan, M.[Michaël], Bloch, I.[Isabelle],
Brain Lesion Detection in 3D PET Images Using Max-Trees and a New Spatial Context Criterion,
ISMM17(455-466).
Springer DOI 1706
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Bento, M.[Mariana], Sym, Y.[Yan], Frayne, R.[Richard], Lotufo, R.[Roberto], Rittner, L.[Letícia],
Probabilistic Segmentation of Brain White Matter Lesions Using Texture-Based Classification,
ICIAR17(71-78).
Springer DOI 1706
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Salvador, R., Fabelo, H., Lazcano, R., Ortega, S., Madroñal, D., Callicó, G.M., Juárez, E., Sanz, C.,
Demo: HELICoiD tool demonstrator for real-time brain cancer detection,
DASIP16(237-238)
IEEE DOI 1704
biological tissues BibRef

Mukherjee, S.[Sabyasachi], Bandyopadhyay, O.[Oishila], Biswas, A.[Arindam],
Automated Brain Tumor Diagnosis and Severity Analysis from Brain MRI,
CompIMAGE16(194-207).
Springer DOI 1704
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Réjichi, S., Chaabane, F.,
Brain tumor extraction using graph based classification of MRI time series for diagnostic assistance,
ISIVC16(320-324)
IEEE DOI 1704
Feature extraction BibRef

Jaroudi, R.[Rym], Baravdish, G.[George], Åström, F.[Freddie], Johansson, B.T.[B. Tomas],
Source Localization of Reaction-Diffusion Models for Brain Tumors,
GCPR16(414-425).
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Dvorák, P.[Pavel], Menze, B.H.[Bjoern H.],
Local Structure Prediction with Convolutional Neural Networks for Multimodal Brain Tumor Segmentation,
MCV15(59-71).
Springer DOI 1608
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Automated Localization of Brain Tumors in MRI Using Potential-K-Means Clustering Algorithm,
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MMBIA01(xx-yy). 0110
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