Table of Contents

• Lightweight U-Nets for Brain Tumor SegmentationTomasz Tarasiewicz, Michal Kawulok, Jakub Nalepa. 3-14 [doi]
• Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised LearnersBeenitaben Shah, Harish Tayyar Madabushi. 15-29 [doi]
• Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder NetworksMohammadreza Soltaninejad, Tony P. Pridmore, Michael P. Pound. 30-39 [doi]
• Trialing U-Net Training Modifications for Segmenting Gliomas Using Open Source Deep Learning FrameworkDavid G. Ellis, Michele R. Aizenberg. 40-49 [doi]
• HI-Net: Hyperdense Inception 3D UNet for Brain Tumor SegmentationSaqib Qamar, Parvez Ahmad, LinLin Shen. 50-57 [doi]
• 2NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation TaskHaozhe Jia, Weidong Cai 0001, Heng Huang, Yong Xia. 58-68 [doi]
• 2D Dense-UNet: A Clinically Valid Approach to Automated Glioma SegmentationHugh McHugh, Gonzalo D. Maso Talou, Alan Wang. 69-80 [doi]
• Attention U-Net with Dimension-Hybridized Fast Data Density Functional Theory for Automatic Brain Tumor Image SegmentationZi-Jun Su, Tang-Chen Chang, Yen-Ling Tai, Shu-Jung Chang, Chien-Chang Chen. 81-92 [doi]
• MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor SegmentationChangchen Zhao, Zhiming Zhao, Qingrun Zeng, Yuanjing Feng. 93-103 [doi]
• Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-ProcessingRichard Zsamboki, Petra Takacs, Borbála Deák-Karancsi. 104-117 [doi]
• nnU-Net for Brain Tumor SegmentationFabian Isensee, Paul F. Jäger, Peter M. Full, Philipp Vollmuth, Klaus H. Maier-Hein. 118-132 [doi]
• A Deep Random Forest Approach for Multimodal Brain Tumor SegmentationSameer Shaikh, Ashish Phophalia. 133-147 [doi]
• Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Data PreprocessingVladimir Groza, Bair Tuchinov, Evgeniya Amelina, Evgeniy Pavlovskiy, Nikolay Tolstokulakov, Mikhail Amelin, Sergey Golushko, Andrey Letyagin. 148-157 [doi]
• A Deep Supervision CNN Network for Brain Tumor SegmentationShiqiang Ma, Zehua Zhang, Jiaqi Ding, Xuejian Li, Jijun Tang, Fei Guo. 158-167 [doi]
• Multi-threshold Attention U-Net (MTAU) Based Model for Multimodal Brain Tumor Segmentation in MRI ScansNavchetan Awasthi, Rohit Pardasani, Swati Gupta. 168-178 [doi]
• Multi-stage Deep Layer Aggregation for Brain Tumor SegmentationCarlos A. Silva 0002, Adriano Pinto, Sérgio Pereira, Ana Lopes. 179-188 [doi]
• Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features FusionMuhammad Junaid Ali, Muhammad Tahir Akram, Hira Saleem, Basit Raza, Ahmad Raza Shahid. 189-199 [doi]
• Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for Brain Tumor Segmentation: BraTS 2020 ChallengeLucas Fidon, Sébastien Ourselin, Tom Vercauteren. 200-214 [doi]
• 3D Semantic Segmentation of Brain Tumor for Overall Survival PredictionRupal R. Agravat, Mehul S. Raval. 215-227 [doi]
• Segmentation, Survival Prediction, and Uncertainty Estimation of Gliomas from Multimodal 3D MRI Using Selective Kernel NetworksJay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Bruce R. Rosen, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer. 228-240 [doi]
• 3D Brain Tumor Segmentation and Survival Prediction Using Ensembles of Convolutional Neural NetworksS. Rosas González, Ilyess Zemmoura, Clovis Tauber. 241-254 [doi]
• Brain Tumour Segmentation Using Probabilistic U-NetChinmay Savadikar, Rahul Kulhalli, Bhushan Garware. 255-264 [doi]
• Segmenting Brain Tumors from MRI Using Cascaded 3D U-NetsKrzysztof Kotowski, Szymon Adamski, Wojciech Malara, Bartosz Machura, Lukasz Zarudzki, Jakub Nalepa. 265-277 [doi]
• A Deep Supervised U-Attention Net for Pixel-Wise Brain Tumor SegmentationJiahua Xu, Wai Po Kevin Teng, Xiong Jun Wang, Andreas Nürnberger. 278-289 [doi]
• A Two-Stage Atrous Convolution Neural Network for Brain Tumor Segmentation and Survival PredictionRadu Miron 0002, Ramona Albert, Mihaela Breaban. 290-299 [doi]
• TwoPath U-Net for Automatic Brain Tumor Segmentation from Multimodal MRI DataKeerati Kaewrak, John J. Soraghan, Gaetano Di Caterina, Derek Grose. 300-309 [doi]
• Brain Tumor Segmentation and Survival Prediction Using Automatic Hard Mining in 3D CNN ArchitectureVikas Kumar Anand, Sanjeev Grampurohit, Pranav Aurangabadkar, Avinash Kori, Mahendra Khened, Raghavendra S. Bhat, Ganapathy Krishnamurthi. 310-319 [doi]
• Some New Tricks for Deep Glioma SegmentationChase Duncan, Francis Roxas, Neel Jani, Jane Maksimovic, Matthew Bramlet, Brad Sutton, Sanmi Koyejo. 320-330 [doi]
• PieceNet: A Redundant UNet EnsembleVikas L. Bommineni. 331-341 [doi]
• Cerberus: A Multi-headed Network for Brain Tumor SegmentationLaura Alexandra Daza, Catalina Gómez, Pablo Arbeláez. 342-351 [doi]
• An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients Based on Volumetric and Shape FeaturesLina Chato, Pushkin Kachroo, Shahram Latifi. 352-365 [doi]
• Squeeze-and-Excitation Normalization for Brain Tumor SegmentationAndrei Iantsen, Vincent Jaouen, Dimitris Visvikis, Mathieu Hatt. 366-373 [doi]
• Modified MobileNet for Patient Survival PredictionAgus Subhan Akbar, Chastine Fatichah, Nanik Suciati. 374-387 [doi]
• Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor SegmentationMihir Pendse, Vithursan Thangarasa, Vitaliy Chiley, Ryan Holmdahl, Joel Hestness, Dennis DeCoste. 388-397 [doi]
• Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-NetBhavesh Parmar, Mehul Parikh. 398-409 [doi]
• DR-Unet104 for Multimodal MRI Brain Tumor SegmentationJordan Colman, Lei Zhang 0043, Wenting Duan, Xujiong Ye. 410-419 [doi]
• Glioma Sub-region Segmentation on Multi-parameter MRI with Label DropoutKun Cheng, Caihao Hu, Pengyu Yin, Qianlan Su, Guancheng Zhou, Xian Wu, Xiaohui Wang, Wei Yang. 420-430 [doi]
• Variational-Autoencoder Regularized 3D MultiResUNet for the BraTS 2020 Brain Tumor SegmentationJiarui Tang, Tengfei Li, Hai Shu, Hongtu Zhu. 431-440 [doi]
• Learning Dynamic Convolutions for Multi-modal 3D MRI Brain Tumor SegmentationQiushi Yang, Yixuan Yuan. 441-451 [doi]
• Automatic Glioma Grading Based on Two-Stage Networks by Integrating Pathology and MRI ImagesXiyue Wang, Sen Yang, Xiyi Wu. 455-464 [doi]
• Brain Tumor Classification Based on MRI Images and Noise Reduced Pathology ImagesBaocai Yin, Hu Cheng, Fengyan Wang, Zengfu Wang. 465-474 [doi]
• Multimodal Brain Tumor ClassificationMarvin Lerousseau, Eric Deutsch, Nikos Paragios. 475-486 [doi]
• A Hybrid Convolutional Neural Network Based-Method for Brain Tumor Classification Using mMRI and WSILinmin Pei, Wei-Wen Hsu, Ling-An Chiang, Jing-Ming Guo, Khan M. Iftekharuddin, Rivka Colen. 487-496 [doi]
• CNN-Based Fully Automatic Glioma Classification with Multi-modal Medical ImagesBingchao Zhao, Jia Huang, Changhong Liang, Zaiyi Liu, Chu Han. 497-507 [doi]
• Glioma Classification Using Multimodal Radiology and Histology DataAzam Hamidinekoo, Tomasz Pieciak, Maryam Afzali, Otar Akanyeti, Yinyin Yuan. 508-518 [doi]