Table of Contents

• BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor SegmentationQiran Jia, Hai Shu. 3-14 [doi]
• Optimized U-Net for Brain Tumor SegmentationMichal Futrega, Alexandre Milesi, Michal Marcinkiewicz, Pablo Ribalta. 15-29 [doi]
• MS UNet: Multi-scale 3D UNet for Brain Tumor SegmentationParvez Ahmad, Saqib Qamar, LinLin Shen, Syed Qasim Afser Rizvi, Aamir Ali, Girija Chetty. 30-41 [doi]
• Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI DatabaseYading Yuan. 42-53 [doi]
• Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor SegmentationKamlesh Pawar, Shenjun Zhong, Dilshan Sasanka Goonatillake, Gary F. Egan, Zhaolin Chen. 54-67 [doi]
• Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor SegmentationXiaohong Cai, Shubin Lou, Mingrui Shuai, Zhulin An. 68-79 [doi]
• MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural NetworksBenjamin B. Yan, Yujia Wei, Jaidip Manikrao M. Jagtap, Mana Moassefi, Diana V. Vera Garcia, Yashbir Singh, Sanaz Vahdati, Shahriar Faghani, Bradley J. Erickson, Gian Marco Conte. 80-89 [doi]
• Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRIXue Feng 0001, Harrison Bai, Daniel Kim, Georgios Maragkos, Jan Machaj, Ryan Kellogg. 90-96 [doi]
• Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor SegmentationHai Nguyen-Truong, Quan-Dung Pham. 97-105 [doi]
• HNF-Netv2 for Brain Tumor Segmentation Using Multi-modal MR ImagingHaozhe Jia, Chao Bai, Weidong Cai 0001, Heng Huang, Yong Xia 0001. 106-115 [doi]
• Disparity Autoencoders for Multi-class Brain Tumor SegmentationChandan Ganesh Bangalore Yogananda, Yudhajit Das, Benjamin C. Wagner, Sahil S. Nalawade, Divya Reddy, James Holcomb, Marco C. Pinho, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. 116-124 [doi]
• Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolutionZhifan Jiang, Can Zhao, Xinyang Liu, Marius George Linguraru. 125-137 [doi]
• An Ensemble Approach to Automatic Brain Tumor SegmentationYaying Shi, Christian Micklisch, Erum Mushtaq, Salman Avestimehr, Yonghong Yan 0001, Xiaodong Zhang. 138-148 [doi]
• Quality-Aware Model Ensemble for Brain Tumor SegmentationKang Wang, Haoran Wang, Zeyang Li, Mingyuan Pan, Manning Wang, Shuo Wang, Zhijian Song. 149-162 [doi]
• Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIsMd Mahfuzur Rahman Siddiquee, Andriy Myronenko. 163-172 [doi]
• Extending nn-UNet for Brain Tumor SegmentationHuan Minh Luu, Sung-Hong Park. 173-186 [doi]
• Generalized Wasserstein Dice Loss, Test-Time Augmentation, and Transformers for the BraTS 2021 ChallengeLucas Fidon, Suprosanna Shit, Ivan Ezhov, Johannes C. Paetzold, Sébastien Ourselin, Tom Vercauteren. 187-196 [doi]
• Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRIKrzysztof Kotowski, Szymon Adamski, Bartosz Machura, Lukasz Zarudzki, Jakub Nalepa. 197-209 [doi]
• Deep Learning Based Ensemble Approach for 3D MRI Brain Tumor SegmentationTien-Bach-Thanh Do, Dang-Linh Trinh, Minh-Trieu Tran, Guee-Sang Lee, Soo-Hyung Kim, Hyung Jeong Yang. 210-221 [doi]
• Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape FeaturesSveinn Pálsson, Stefano Cerri, Koen Van Leemput. 222-231 [doi]
• Combining CNNs with Transformer for Multimodal 3D MRI Brain Tumor SegmentationMariia Dobko, Danylo-Ivan Kolinko, Ostap Viniavskyi, Yurii Yelisieiev. 232-241 [doi]
• Brain Tumor Segmentation Using Deep InfomaxJitendra Marndi, Cailyn Craven, Geena Kim. 242-252 [doi]
• Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNormAlexandre Carré, Eric Deutsch, Charlotte Robert. 253-266 [doi]
• Brain Tumor Segmentation with Self-supervised Enhance Region Post-processingSergey Pnev, Vladimir Groza, Bair Tuchinov, Evgeniya Amelina, Evgeniy Pavlovskiy, Nikolay Tolstokulakov, Mihail Amelin, Sergey Golushko, Andrey Letyagin. 267-275 [doi]
• 3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challengeSyed Talha Bukhari, Hassan Mohy-ud-Din. 276-288 [doi]
• Brain Tumor Segmentation from Multiparametric MRI Using a Multi-encoder U-Net ArchitectureSaruar Alam, Bharath Halandur, P. G. L. Porta Mana, Dorota Goplen, Arvid Lundervold, Alexander Selvikvåg Lundervold. 289-301 [doi]
• AttU-NET: Attention U-Net for Brain Tumor SegmentationSihan Wang, Lei Li, Xiahai Zhuang. 302-311 [doi]
• Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net ArchitectureSatyajit Maurya, Virendra Kumar Yadav, Sumeet Agarwal, Anup Singh. 312-323 [doi]
• Neural Network Based Brain Tumor SegmentationDarshat Shah, Avishek Biswas, Pranali Sonpatki, Sunder Chakravarty, Nameeta Shah. 324-333 [doi]
• Brain Tumor Segmentation (BraTS) Challenge Short Paper: Improving Three-Dimensional Brain Tumor Segmentation Using SegResnet and Hybrid Boundary-Dice LossCheyu Hsu, Chun-Hao Chang, Tom Weiwu Chen, Hsinhan Tsai, Shihchieh Ma, Weichung Wang. 334-344 [doi]
• A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRIAleksandr Emchinov. 345-356 [doi]
• Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized HyperparametersWalia Farzana, Ahmed G. Temtam, Zeina A. Shboul, Md Monibor Rahman, M. Shibly Sadique, Khan M. Iftekharuddin. 357-366 [doi]
• Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT PredictionDaniel Abler, Vincent Andrearczyk, Valentin Oreiller, Javier Barranco Garcia, Diem Vuong, Stephanie Tanadini-Lang, Matthias Guckenberger, Mauricio Reyes 0001, Adrien Depeursinge. 367-380 [doi]
• FedCostWAvg: A New Averaging for Better Federated LearningLeon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Claus Zimmer, Benedikt Wiestler, Bjoern H. Menze. 383-391 [doi]
• Federated Learning Using Variable Local Training for Brain Tumor SegmentationAnup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert. 392-404 [doi]
• Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor SegmentationEce Isik-Polat, Gorkem Polat, Altan Koçyigit, Alptekin Temizel. 405-419 [doi]
• Multi-institutional Travelling Model for Tumor Segmentation in MRI DatasetsRaissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert. 420-432 [doi]
• Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client PruningYoutan Yin, Hongzheng Yang, Quande Liu, Meirui Jiang, Cheng Chen 0013, Qi Dou, Pheng-Ann Heng. 433-443 [doi]
• Federated Learning for Brain Tumor Segmentation Using MRI and TransformersSahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Divya Reddy, Yudhajit Das, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian. 444-454 [doi]
• Adaptive Weight Aggregation in Federated Learning for Brain Tumor SegmentationMuhammad Irfan Khan, Mojtaba Jafaritadi, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan. 455-469 [doi]
• A Study on Criteria for Training Collaborator Selection in Federated LearningVishruth Shambhat, Akansh Maurya, Shubham Subhas Danannavar, Rohit Kalla, Vikas Kumar Anand, Ganapathy Krishnamurthi. 470-480 [doi]
• Center Dropout: A Simple Method for Speed and Fairness in Federated LearningAkis Linardos, Kaisar Kushibar, Karim Lekadir. 481-493 [doi]
• Brain Tumor Segmentation Using Two-Stage Convolutional Neural Network for Federated EvaluationKamlesh Pawar, Shenjun Zhong, Zhaolin Chen, Gary F. Egan. 494-505 [doi]
• Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An Approach for the CrossMoDA ChallengeJae-Won Choi. 509-517 [doi]
• Unsupervised Cross-modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model EnsembleHao Li, Dewei Hu, Qibang Zhu, Kathleen E. Larson, Huahong Zhang, Ipek Oguz. 518-528 [doi]
• Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label FusionHan Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant. 529-539 [doi]
• nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical ImagingSmriti Joshi, Richard Osuala, Carlos Martín-Isla, Víctor M. Campello, Carla Sendra-Balcells, Karim Lekadir, Sergio Escalera. 540-551 [doi]
• Extending Probabilistic U-Net Using MC-Dropout to Quantify Data and Model UncertaintyIshaan Bhat, Hugo J. Kuijf. 555-559 [doi]
• Holistic Network for Quantifying Uncertainties in Medical ImagesJimut Bahan Pal. 560-569 [doi]
• Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-NetYanwu Yang, Xutao Guo, Yiwei Pan, Pengcheng Shi, Haiyan Lv, Ting Ma 0001. 570-577 [doi]
• Meta-learning for Medical Image Segmentation Uncertainty QuantificationSabri Can Cetindag, Mert Yergin, Deniz Alis, Ilkay Öksüz. 578-584 [doi]
• Using Soft Labels to Model Uncertainty in Medical Image SegmentationJoão Lourenço Silva, Arlindo L. Oliveira. 585-596 [doi]