3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural Networks (Englisch)
- Neue Suche nach: González, S. Rosas
- Neue Suche nach: Zemmoura, I.
- Neue Suche nach: Tauber, C.
- Neue Suche nach: Crimi, Alessandro
- Weitere Informationen zu Crimi, Alessandro:
- https://orcid.org/https://orcid.org/0000-0001-5397-6363
- Neue Suche nach: Bakas, Spyridon
- Weitere Informationen zu Bakas, Spyridon:
- https://orcid.org/https://orcid.org/0000-0001-8734-6482
- Neue Suche nach: González, S. Rosas
- Neue Suche nach: Zemmoura, I.
- Neue Suche nach: Tauber, C.
In:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
: 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II
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Kapitel: 21
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241-254
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2021
- Aufsatz/Kapitel (Buch) / Elektronische Ressource
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Titel:3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural Networks
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Weitere Titelangaben:Lect.Notes Computer
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Beteiligte:Crimi, Alessandro ( Herausgeber:in ) / Bakas, Spyridon ( Herausgeber:in ) / González, S. Rosas ( Autor:in ) / Zemmoura, I. ( Autor:in ) / Tauber, C. ( Autor:in )
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Kongress:International MICCAI Brainlesion Workshop ; 2020 ; Lima, Peru
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Erschienen in:Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 6th International Workshop, BrainLes 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers, Part II ; Kapitel: 21 ; 241-254Lecture Notes in Computer Science ; 12659 ; 241-254
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Verlag:
- Neue Suche nach: Springer International Publishing
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Erscheinungsort:Cham
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Erscheinungsdatum:26.03.2021
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Format / Umfang:14 pages
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ISBN:
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ISSN:
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DOI:
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Medientyp:Aufsatz/Kapitel (Buch)
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Format:Elektronische Ressource
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Sprache:Englisch
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Schlagwörter:
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Datenquelle:
Inhaltsverzeichnis E-Book
Die Inhaltsverzeichnisse werden automatisch erzeugt und basieren auf den im Index des TIB-Portals verfügbaren Einzelnachweisen der enthaltenen Beiträge. Die Anzeige der Inhaltsverzeichnisse kann daher unvollständig oder lückenhaft sein.
- 1
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Lightweight U-Nets for Brain Tumor SegmentationTarasiewicz, Tomasz / Kawulok, Michal / Nalepa, Jakub et al. | 2021
- 2
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Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised LearnersShah, Beenitaben / Madabushi, Harish Tayyar et al. | 2021
- 3
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Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder NetworksSoltaninejad, Mohammadreza / Pridmore, Tony / Pound, Michael et al. | 2021
- 4
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Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning FrameworkEllis, David G. / Aizenberg, Michele R. et al. | 2021
- 5
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HI-Net: Hyperdense Inception 3D UNet for Brain Tumor SegmentationQamar, Saqib / Ahmad, Parvez / Shen, Linlin et al. | 2021
- 6
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H\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^2$$\end{document}NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation TaskJia, Haozhe / Cai, Weidong / Huang, Heng / Xia, Yong et al. | 2021
- 7
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2D Dense-UNet: A Clinically Valid Approach to Automated Glioma SegmentationMcHugh, Hugh / Talou, Gonzalo Maso / Wang, Alan et al. | 2021
- 8
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Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image SegmentationSu, Zi-Jun / Chang, Tang-Chen / Tai, Yen-Ling / Chang, Shu-Jung / Chen, Chien-Chang et al. | 2021
- 9
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MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor SegmentationZhao, Changchen / Zhao, Zhiming / Zeng, Qingrun / Feng, Yuanjing et al. | 2021
- 10
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Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-ProcessingZsamboki, Richard / Takacs, Petra / Deak-Karancsi, Borbala et al. | 2021
- 11
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nnU-Net for Brain Tumor SegmentationIsensee, Fabian / Jäger, Paul F. / Full, Peter M. / Vollmuth, Philipp / Maier-Hein, Klaus H. et al. | 2021
- 12
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A Deep Random Forest Approach for Multimodal Brain Tumor SegmentationShaikh, Sameer / Phophalia, Ashish et al. | 2021
- 13
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Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data PreprocessingGroza, Vladimir / Tuchinov, Bair / Amelina, Evgeniya / Pavlovskiy, Evgeniy / Tolstokulakov, Nikolay / Amelin, Mikhail / Golushko, Sergey / Letyagin, Andrey et al. | 2021
- 14
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A Deep Supervision CNN Network for Brain Tumor SegmentationMa, Shiqiang / Zhang, Zehua / Ding, Jiaqi / Li, Xuejian / Tang, Jijun / Guo, Fei et al. | 2021
- 15
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Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI ScansAwasthi, Navchetan / Pardasani, Rohit / Gupta, Swati et al. | 2021
- 16
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Multi-stage Deep Layer Aggregation for Brain Tumor SegmentationSilva, Carlos A. / Pinto, Adriano / Pereira, Sérgio / Lopes, Ana et al. | 2021
- 17
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Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features FusionAli, Muhammad Junaid / Akram, Muhammad Tahir / Saleem, Hira / Raza, Basit / Shahid, Ahmad Raza et al. | 2021
- 18
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Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 ChallengeFidon, Lucas / Ourselin, Sébastien / Vercauteren, Tom et al. | 2021
- 19
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3D Semantic Segmentation of Brain Tumor for Overall Survival PredictionAgravat, Rupal R. / Raval, Mehul S. et al. | 2021
- 20
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Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel NetworksPatel, Jay / Chang, Ken / Hoebel, Katharina / Gidwani, Mishka / Arun, Nishanth / Gupta, Sharut / Aggarwal, Mehak / Singh, Praveer / Rosen, Bruce R. / Gerstner, Elizabeth R. et al. | 2021
- 21
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3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural NetworksGonzález, S. Rosas / Zemmoura, I. / Tauber, C. et al. | 2021
- 22
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Brain Tumour Segmentation Using Probabilistic U-NetSavadikar, Chinmay / Kulhalli, Rahul / Garware, Bhushan et al. | 2021
- 23
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Segmenting Brain Tumors from MRI Using Cascaded 3D U-NetsKotowski, Krzysztof / Adamski, Szymon / Malara, Wojciech / Machura, Bartosz / Zarudzki, Lukasz / Nalepa, Jakub et al. | 2021
- 24
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A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor SegmentationXu, Jia Hua / Teng, Wai Po Kevin / Wang, Xiong Jun / Nürnberger, Andreas et al. | 2021
- 25
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A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival PredictionMiron, Radu / Albert, Ramona / Breaban, Mihaela et al. | 2021
- 26
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TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI DataKaewrak, Keerati / Soraghan, John / Di Caterina, Gaetano / Grose, Derek et al. | 2021
- 27
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Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN ArchitectureAnand, Vikas Kumar / Grampurohit, Sanjeev / Aurangabadkar, Pranav / Kori, Avinash / Khened, Mahendra / Bhat, Raghavendra S. / Krishnamurthi, Ganapathy et al. | 2021
- 28
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Some New Tricks for Deep Glioma SegmentationDuncan, Chase / Roxas, Francis / Jani, Neel / Maksimovic, Jane / Bramlet, Matthew / Sutton, Brad / Koyejo, Sanmi et al. | 2021
- 29
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PieceNet: A Redundant UNet EnsembleBommineni, Vikas L. et al. | 2021
- 30
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Cerberus: A Multi-headed Network for Brain Tumor SegmentationDaza, Laura / Gómez, Catalina / Arbeláez, Pablo et al. | 2021
- 31
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An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients Based on Volumetric and Shape FeaturesChato, Lina / Kachroo, Pushkin / Latifi, Shahram et al. | 2021
- 32
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Squeeze-and-Excitation Normalization for Brain Tumor SegmentationIantsen, Andrei / Jaouen, Vincent / Visvikis, Dimitris / Hatt, Mathieu et al. | 2021
- 33
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Modified MobileNet for Patient Survival PredictionAkbar, Agus Subhan / Fatichah, Chastine / Suciati, Nanik et al. | 2021
- 34
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Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor SegmentationPendse, Mihir / Thangarasa, Vithursan / Chiley, Vitaliy / Holmdahl, Ryan / Hestness, Joel / DeCoste, Dennis et al. | 2021
- 35
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Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-NetParmar, Bhavesh / Parikh, Mehul et al. | 2021
- 36
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DR-Unet104 for Multimodal MRI Brain Tumor SegmentationColman, Jordan / Zhang, Lei / Duan, Wenting / Ye, Xujiong et al. | 2021
- 37
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Glioma Sub-region Segmentation on Multi-parameter MRI with Label DropoutCheng, Kun / Hu, Caihao / Yin, Pengyu / Su, Qianlan / Zhou, Guancheng / Wu, Xian / Wang, Xiaohui / Yang, Wei et al. | 2021
- 38
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Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor SegmentationTang, Jiarui / Li, Tengfei / Shu, Hai / Zhu, Hongtu et al. | 2021
- 39
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Learning Dynamic Convolutions for Multi-modal 3D MRI Brain Tumor SegmentationYang, Qiushi / Yuan, Yixuan et al. | 2021
- 40
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Automatic Glioma Grading Based on Two-Stage Networks by Integrating Pathology and MRI ImagesWang, Xiyue / Yang, Sen / Wu, Xiyi et al. | 2021
- 41
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Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology ImagesYin, Baocai / Cheng, Hu / Wang, Fengyan / Wang, Zengfu et al. | 2021
- 42
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Multimodal Brain Tumor ClassificationLerousseau, Marvin / Deutsch, Eric / Paragios, Nikos et al. | 2021
- 43
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A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSIPei, Linmin / Hsu, Wei-Wen / Chiang, Ling-An / Guo, Jing-Ming / Iftekharuddin, Khan M. / Colen, Rivka et al. | 2021
- 44
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CNN-Based Fully Automatic Glioma Classification with Multi-modal Medical ImagesZhao, Bingchao / Huang, Jia / Liang, Changhong / Liu, Zaiyi / Han, Chu et al. | 2021
- 45
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Glioma Classification Using Multimodal Radiology and Histology DataHamidinekoo, Azam / Pieciak, Tomasz / Afzali, Maryam / Akanyeti, Otar / Yuan, Yinyin et al. | 2021