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ORCID8th Brainles@MICCAI 2022: Singapore - Part II
- Spyridon Bakas
, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - 8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers, Part II. Lecture Notes in Computer Science 14092, Springer 2023, ISBN 978-3-031-44152-3
BraTS-Reg
- Kewei Yan, Yonghong Yan:
Applying Quadratic Penalty Method for Intensity-Based Deformable Image Registration on BraTS-Reg Challenge 2022. 3-14 - Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi:
WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network. 15-24 - Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert:
Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients. 25-34 - Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt:
3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors. 35-45
CrossMoDa
- Luyi Han, Yunzhi Huang, Tao Tan, Ritse Mann:
Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction Based on Semi-supervised Contrastive Learning. 49-58 - Tao Yang, Lisheng Wang:
Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation. 59-67 - Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo, Xulei Yang, Cuntai Guan:
MS-MT: Multi-scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation. 68-78 - Yuzhou Zhuang, Hong Liu, Enmin Song, Coskun Cetinkaya, Chih-Cheng Hung:
An Unpaired Cross-Modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea. 79-89 - Shahad Hardan, Hussain Alasmawi, Xiangjian Hou, Mohammad Yaqub:
Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation. 90-99 - Bogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, Tae-Eui Kam:
Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation. 100-108 - Han Liu, Yubo Fan, Ipek Oguz, Benoit M. Dawant:
Enhancing Data Diversity for Self-training Based Unsupervised Cross-Modality Vestibular Schwannoma and Cochlea Segmentation. 109-118
FeTS
- Muhammad Irfan Khan, Mohammad Ayyaz Azeem, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi:
Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation. 121-132 - Gaurav Singh:
A Local Score Strategy for Weight Aggregation in Federated Learning. 133-141 - Jianxun Ren, Wei Zhang, Ning An, Qingyu Hu, Youjia Zhang, Ying Zhou:
Ensemble Outperforms Single Models in Brain Tumor Segmentation. 142-153 - Vasilis Siomos, Giacomo Tarroni, Jonathan Passerat-Palmbach:
FeTS Challenge 2022 Task 1: Implementing FedMGDA + and a New Partitioning. 154-160 - Meirui Jiang, Hongzheng Yang, Xiaofan Zhang, Shaoting Zhang, Qi Dou:
Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client Pruning. 161-172 - Himashi Peiris, Munawar Hayat, Zhaolin Chen, Gary F. Egan, Mehrtash Harandi:
Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation. 173-182 - Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin:
Robust Learning Protocol for Federated Tumor Segmentation Challenge. 183-195 - Yuan Wang, Renuga Kanagavelu, Qingsong Wei, Yechao Yang, Yong Liu:
Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data Distribution. 196-208 - Leon Mächler, Ivan Ezhov, Suprosanna Shit, Johannes C. Paetzold:
FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated Learning. 209-217 - Krzysztof Kotowski, Szymon Adamski, Bartosz Machura, Wojciech Malara, Lukasz Zarudzki, Jakub Nalepa:
Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor Segmentation. 218-227 - Yaying Shi, Hongjian Gao, Salman Avestimehr, Yonghong Yan:
Experimenting FedML and NVFLARE for Federated Tumor Segmentation Challenge. 228-240
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