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7th Brainles@MICCAI 2021: Virtual Event - Part I
- 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 I. Lecture Notes in Computer Science 12962, Springer 2022, ISBN 978-3-031-08998-5
Invited Papers
- Alexander Chowdhury, Hasan Kassem, Nicolas Padoy, Renato Umeton, Alexandros Karargyris:
A Review of Medical Federated Learning: Applications in Oncology and Cancer Research. 3-24 - Jay B. Patel, Ken Chang, Syed Rakin Ahmed
Opportunities and Challenges for Deep Learning in Brain Lesions. 25-36
Brain Lesions
- Abhinav Sagar:
EMSViT: Efficient Multi Scale Vision Transformer for Biomedical Image Segmentation. 39-51 - Baocai Yin, Hu Cheng, Fengyan Wang, Zengfu Wang:
CA-Net: Collaborative Attention Network for Multi-modal Diagnosis of Gliomas. 52-62 - Felix Meissen, Georgios Kaissis, Daniel Rueckert:
Challenging Current Semi-supervised Anomaly Segmentation Methods for Brain MRI. 63-74 - Alex Wong
Small Lesion Segmentation in Brain MRIs with Subpixel Embedding. 75-87 - Guillaume Pelluet
Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentation. 88-99 - Karin A. van Garderen, Sebastian R. van der Voort
Evaluating Glioma Growth Predictions as a Forward Ranking Problem. 100-111 - Martin Zukovec, Lara Dular, Ziga Spiclin:
Modeling Multi-annotator Uncertainty as Multi-class Segmentation Problem. 112-123 - Yifan Li
Adaptive Unsupervised Learning with Enhanced Feature Representation for Intra-tumor Partitioning and Survival Prediction for Glioblastoma. 124-139 - Yiran Wei, Yonghao Li, Xi Chen, Carola-Bibiane Schönlieb, Chao Li
Predicting Isocitrate Dehydrogenase Mutation Status in Glioma Using Structural Brain Networks and Graph Neural Networks. 140-150 - Siddhesh P. Thakur
Optimization of Deep Learning Based Brain Extraction in MRI for Low Resource Environments. 151-167
BraTS
- Himashi Peiris
Reciprocal Adversarial Learning for Brain Tumor Segmentation: A Solution to BraTS Challenge 2021 Segmentation Task. 171-181 - Agus Subhan Akbar
Unet3D with Multiple Atrous Convolutions Attention Block for Brain Tumor Segmentation. 182-193 - Polina Druzhinina, Ekaterina Kondrateva, Arseny Bozhenko, Vyacheslav Yarkin, Maxim Sharaev, Anvar Kurmukov:
BRATS2021: Exploring Each Sequence in Multi-modal Input for Baseline U-net Performance. 194-203 - Hua Yang, Zhiqiang Shen, Zhaopei Li, Jinqing Liu, Jinchao Xiao:
Combining Global Information with Topological Prior for Brain Tumor Segmentation. 204-215 - Zhaopei Li, Zhiqiang Shen, Jianhui Wen, Tian He, Lin Pan:
Automatic Brain Tumor Segmentation Using Multi-scale Features and Attention Mechanism. 216-226 - Daniel Tianming Chen
Simple and Fast Convolutional Neural Network Applied to Median Cross Sections for Predicting the Presence of MGMT Promoter Methylation in FLAIR MRI Scans. 227-238 - Zhenzhen Dai
Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model Ensemble. 239-248 - Quoc-Huy Trinh, Trong-Hieu Nguyen Mau, Radmir Zosimov, Minh-Van Nguyen:
EfficientNet for Brain-Lesion Classification. 249-260 - Hung-Yu Wu, Youn-Long Lin:
HarDNet-BTS: A Harmonic Shortcut Network for Brain Tumor Segmentation. 261-271 - Ali Hatamizadeh, Vishwesh Nath, Yucheng Tang, Dong Yang, Holger R. Roth, Daguang Xu:
Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. 272-284 - Johannes Roth, Johannes Keller, Stefan Franke, Thomas Neumuth, Daniel Schneider:
Multi-plane UNet++ Ensemble for Glioblastoma Segmentation. 285-294 - Gaurav Singh
Multimodal Brain Tumor Segmentation Using Modified UNet Architecture. 295-305 - Daniel M. Lang
A Video Data Based Transfer Learning Approach for Classification of MGMT Status in Brain Tumor MR Images. 306-314 - Linmin Pei, Yanling Liu:
Multimodal Brain Tumor Segmentation Using a 3D ResUNet in BraTS 2021. 315-323 - Vladimir S. Fonov
3D MRI Brain Tumour Segmentation with Autoencoder Regularization and Hausdorff Distance Loss Function. 324-332 - Yoonseok Choi, Mohammed A. Al-masni
3D CMM-Net with Deeper Encoder for Semantic Segmentation of Brain Tumors in BraTS2021 Challenge. 333-343 - Timothy Sum Hon Mun
Multi Modal Fusion for Radiogenomics Classification of Brain Tumor. 344-355 - Camillo Saueressig
A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation. 356-365 - Alexandre Milesi, Michal Futrega, Michal Marcinkiewicz, Pablo Ribalta:
Brain Tumor Segmentation Using Neural Network Topology Search. 366-376 - Nabil Jabareen
Segmenting Brain Tumors in Multi-modal MRI Scans Using a 3D SegNet Architecture. 377-388 - Marc Demoustier
Residual 3D U-Net with Localization for Brain Tumor Segmentation. 389-399 - Wen-Wei Lin, Tiexiang Li, Tsung-Ming Huang
A Two-Phase Optimal Mass Transportation Technique for 3D Brain Tumor Detection and Segmentation. 400-409 - Minh Sao Khue Luu
Cascaded Training Pipeline for 3D Brain Tumor Segmentation. 410-420 - Jun Ma
NnUNet with Region-based Training and Loss Ensembles for Brain Tumor Segmentation. 421-430 - Har Shwinder Singh:
Brain Tumor Segmentation Using Attention Activated U-Net with Positive Mining. 431-440 - Yang Yang, Shuhang Wei, Dingwen Zhang, Qingsen Yan, Shijie Zhao, Junwei Han:
Hierarchical and Global Modality Interaction for Brain Tumor Segmentation. 441-450 - Jianxun Ren, Wei Zhang, Ning An, Qingyu Hu, Youjia Zhang, Ying Zhou:
Ensemble Outperforms Single Models in Brain Tumor Segmentation. 451-462 - Md Monibor Rahman, Md. Shibly Sadique, Ahmed G. Temtam, Walia Farzana, Lasitha Vidyaratne, Khan M. Iftekharuddin:
Brain Tumor Segmentation Using UNet-Context Encoding Network. 463-472 - Ramy A. Zeineldin
Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI. 473-483
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