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ORCID7th Brainles@MICCAI 2021: Virtual Event - Part II
- Alessandro Crimi
, Spyridon Bakas:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II. Lecture Notes in Computer Science 12963, Springer 2022, ISBN 978-3-031-09001-1
BraTS
- Qiran Jia, Hai Shu:
BiTr-Unet: A CNN-Transformer Combined Network for MRI Brain Tumor Segmentation. 3-14 - Michal Futrega, Alexandre Milesi, Michal Marcinkiewicz, Pablo Ribalta:
Optimized U-Net for Brain Tumor Segmentation. 15-29 - Parvez Ahmad, Saqib Qamar, Linlin Shen, Syed Qasim Afser Rizvi, Aamir Ali, Girija Chetty:
MS UNet: Multi-scale 3D UNet for Brain Tumor Segmentation. 30-41 - Yading Yuan:
Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database. 42-53 - Kamlesh Pawar, Shenjun Zhong, Dilshan Sasanka Goonatillake, Gary F. Egan, Zhaolin Chen:
Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor Segmentation. 54-67 - Xiaohong Cai, Shubin Lou, Mingrui Shuai, Zhulin An:
Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation. 68-79 - Benjamin 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:
MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks. 80-89 - Xue Feng, Harrison X. Bai, Daniel Kim, Georgios Maragkos, Jan Machaj, Ryan Kellogg:
Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRI. 90-96 - Hai Nguyen-Truong, Quan-Dung Pham:
Dice Focal Loss with ResNet-like Encoder-Decoder Architecture in 3D Brain Tumor Segmentation. 97-105 - Haozhe Jia, Chao Bai, Weidong Cai, Heng Huang, Yong Xia:
HNF-Netv2 for Brain Tumor Segmentation Using Multi-modal MR Imaging. 106-115 - Chandan Ganesh Bangalore Yogananda, Yudhajit Das, Benjamin C. Wagner, Sahil S. Nalawade, Divya D. Reddy, James M. Holcomb, Marco C. Pinho, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian:
Disparity Autoencoders for Multi-class Brain Tumor Segmentation. 116-124 - Zhifan Jiang, Can Zhao, Xinyang Liu, Marius George Linguraru:
Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging Using Model Ensembling and Super-resolution. 125-137 - Yaying Shi, Christian Micklisch, Erum Mushtaq, Salman Avestimehr, Yonghong Yan, Xiaodong Zhang:
An Ensemble Approach to Automatic Brain Tumor Segmentation. 138-148 - Kang Wang, Haoran Wang, Zeyang Li, Mingyuan Pan, Manning Wang, Shuo Wang, Zhijian Song:
Quality-Aware Model Ensemble for Brain Tumor Segmentation. 149-162 - Md Mahfuzur Rahman Siddiquee, Andriy Myronenko:
Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs. 163-172 - Huan Minh Luu, Sung-Hong Park:
Extending nn-UNet for Brain Tumor Segmentation. 173-186 - Lucas Fidon, Suprosanna Shit, Ivan Ezhov, Johannes C. Paetzold, Sébastien Ourselin, Tom Vercauteren:
Generalized Wasserstein Dice Loss, Test-Time Augmentation, and Transformers for the BraTS 2021 Challenge. 187-196 - Krzysztof Kotowski, Szymon Adamski, Bartosz Machura, Lukasz Zarudzki, Jakub Nalepa:
Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI. 197-209 - Tien-Bach-Thanh Do, Dang-Linh Trinh, Minh-Trieu Tran, Guee-Sang Lee, Soo-Hyung Kim, Hyung-Jeong Yang:
Deep Learning Based Ensemble Approach for 3D MRI Brain Tumor Segmentation. 210-221 - Sveinn Pálsson, Stefano Cerri, Koen Van Leemput:
Prediction of MGMT Methylation Status of Glioblastoma Using Radiomics and Latent Space Shape Features. 222-231 - Mariia Dobko, Danylo-Ivan Kolinko, Ostap Viniavskyi, Yurii Yelisieiev:
Combining CNNs with Transformer for Multimodal 3D MRI Brain Tumor Segmentation. 232-241 - Jitendra Marndi, Cailyn Craven, Geena Kim:
Brain Tumor Segmentation Using Deep Infomax. 242-252 - Alexandre Carré, Eric Deutsch, Charlotte Robert:
Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm. 253-266 - Sergey Pnev, Vladimir Groza, Bair Tuchinov, Evgeniya Amelina, Evgeniy N. Pavlovskiy, Nikolay Tolstokulakov, Mihail Amelin, Sergey Golushko, Andrey Letyagin:
Brain Tumor Segmentation with Self-supervised Enhance Region Post-processing. 267-275 - Syed Talha Bukhari, Hassan Mohy-ud-Din:
E1D3 U-Net for Brain Tumor Segmentation: Submission to the RSNA-ASNR-MICCAI BraTS 2021 challenge. 276-288 - Saruar Alam, Bharath Halandur, P. G. L. Porta Mana, Dorota Goplen, Arvid Lundervold, Alexander Selvikvåg Lundervold:
Brain Tumor Segmentation from Multiparametric MRI Using a Multi-encoder U-Net Architecture. 289-301 - Sihan Wang, Lei Li, Xiahai Zhuang:
AttU-NET: Attention U-Net for Brain Tumor Segmentation. 302-311 - Satyajit Maurya, Virendra Kumar Yadav, Sumeet Agarwal, Anup Singh:
Brain Tumor Segmentation in mpMRI Scans (BraTS-2021) Using Models Based on U-Net Architecture. 312-323 - Darshat Shah, Avishek Biswas, Pranali Sonpatki, Sunder Chakravarty, Nameeta Shah:
Neural Network Based Brain Tumor Segmentation. 324-333 - Cheyu Hsu, Chun-Hao Chang, Tom Weiwu Chen, Hsinhan Tsai, Shihchieh Ma, Weichung Wang:
Brain Tumor Segmentation (BraTS) Challenge Short Paper: Improving Three-Dimensional Brain Tumor Segmentation Using SegResnet and Hybrid Boundary-Dice Loss. 334-344 - Aleksandr Emchinov:
A Deep Learning Approach to Glioblastoma Radiogenomic Classification Using Brain MRI. 345-356 - Walia Farzana, Ahmed G. Temtam, Zeina A. Shboul, Md Monibor Rahman, Md. Shibly Sadique, Khan M. Iftekharuddin:
Radiogenomic Prediction of MGMT Using Deep Learning with Bayesian Optimized Hyperparameters. 357-366 - Daniel Abler, Vincent Andrearczyk, Valentin Oreiller, Javier Barranco Garcia, Diem Vuong, Stephanie Tanadini-Lang, Matthias Guckenberger, Mauricio Reyes, Adrien Depeursinge:
Comparison of MR Preprocessing Strategies and Sequences for Radiomics-Based MGMT Prediction. 367-380
FeTS
- Leon Mächler, Ivan Ezhov, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Timo Loehr, Claus Zimmer, Benedikt Wiestler, Bjoern H. Menze:
FedCostWAvg: A New Averaging for Better Federated Learning. 383-391 - Anup Tuladhar, Lakshay Tyagi, Raissa Souza, Nils D. Forkert:
Federated Learning Using Variable Local Training for Brain Tumor Segmentation. 392-404 - Ece Isik-Polat, Gorkem Polat, Altan Koçyigit, Alptekin Temizel:
Evaluation and Analysis of Different Aggregation and Hyperparameter Selection Methods for Federated Brain Tumor Segmentation. 405-419 - Raissa Souza, Anup Tuladhar, Pauline Mouches, Matthias Wilms, Lakshay Tyagi, Nils D. Forkert:
Multi-institutional Travelling Model for Tumor Segmentation in MRI Datasets. 420-432 - Youtan Yin, Hongzheng Yang, Quande Liu, Meirui Jiang, Cheng Chen, Qi Dou, Pheng-Ann Heng:
Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client Pruning. 433-443 - Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Divya D. Reddy, Yudhajit Das, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian:
Federated Learning for Brain Tumor Segmentation Using MRI and Transformers. 444-454 - Muhammad Irfan Khan, Mojtaba Jafaritadi, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan:
Adaptive Weight Aggregation in Federated Learning for Brain Tumor Segmentation. 455-469 - Vishruth Shambhat, Akansh Maurya, Shubham Subhas Danannavar, Rohit Kalla, Vikas Kumar Anand, Ganapathy Krishnamurthi:
A Study on Criteria for Training Collaborator Selection in Federated Learning. 470-480 - Akis Linardos, Kaisar Kushibar, Karim Lekadir:
Center Dropout: A Simple Method for Speed and Fairness in Federated Learning. 481-493 - Kamlesh Pawar, Shenjun Zhong, Zhaolin Chen, Gary F. Egan:
Brain Tumor Segmentation Using Two-Stage Convolutional Neural Network for Federated Evaluation. 494-505
CrossMoDA
- Jae Won Choi:
Using Out-of-the-Box Frameworks for Contrastive Unpaired Image Translation for Vestibular Schwannoma and Cochlea Segmentation: An Approach for the CrossMoDA Challenge. 509-517 - Hao Li, Dewei Hu, Qibang Zhu, Kathleen E. Larson, Huahong Zhang, Ipek Oguz:
Unsupervised Cross-modality Domain Adaptation for Segmenting Vestibular Schwannoma and Cochlea with Data Augmentation and Model Ensemble. 518-528 - Han Liu, Yubo Fan, Can Cui, Dingjie Su, Andrew McNeil, Benoit M. Dawant:
Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion. 529-539 - Smriti Joshi, Richard Osuala, Carlos Martín-Isla, Víctor M. Campello, Carla Sendra-Balcells, Karim Lekadir, Sergio Escalera:
nn-UNet Training on CycleGAN-Translated Images for Cross-modal Domain Adaptation in Biomedical Imaging. 540-551
QUBIQ
- Ishaan Bhat, Hugo J. Kuijf:
Extending Probabilistic U-Net Using MC-Dropout to Quantify Data and Model Uncertainty. 555-559 - Jimut Bahan Pal:
Holistic Network for Quantifying Uncertainties in Medical Images. 560-569 - Yanwu Yang, Xutao Guo, Yiwei Pan, Pengcheng Shi, Haiyan Lv, Ting Ma:
Uncertainty Quantification in Medical Image Segmentation with Multi-decoder U-Net. 570-577 - Sabri Can Cetindag, Mert Yergin, Deniz Alis, Ilkay Öksüz:
Meta-learning for Medical Image Segmentation Uncertainty Quantification. 578-584 - João Lourenço Silva, Arlindo L. Oliveira:
Using Soft Labels to Model Uncertainty in Medical Image Segmentation. 585-596
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