Abstract is missing.
- Applying Quadratic Penalty Method for Intensity-Based Deformable Image Registration on BraTS-Reg Challenge 2022Kewei Yan, Yonghong Yan 0001. 3-14 [doi]
- WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration NetworkSahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi. 15-24 [doi]
- Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma PatientsRamy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert. 25-34 [doi]
- 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain TumorsJavid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt. 35-45 [doi]
- Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction Based on Semi-supervised Contrastive LearningLuyi Han, Yunzhi Huang, Tao Tan, Ritse Mann. 49-58 [doi]
- Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain AdaptationTao Yang, Lisheng Wang. 59-67 [doi]
- MS-MT: Multi-scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea SegmentationZiyuan Zhao, Kaixin Xu, Huai Zhe Yeo, XuLei Yang, Cuntai Guan. 68-78 [doi]
- An Unpaired Cross-Modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and CochleaYuzhou Zhuang, Hong Liu 0005, Enmin Song, Coskun Cetinkaya, Chih-Cheng Hung. 79-89 [doi]
- Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma SegmentationShahad Hardan, Hussain Alasmawi, Xiangjian Hou, Mohammad Yaqub. 90-99 [doi]
- Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea SegmentationBogyeong Kang, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, Tae-Eui Kam. 100-108 [doi]
- Enhancing Data Diversity for Self-training Based Unsupervised Cross-Modality Vestibular Schwannoma and Cochlea SegmentationHan Liu, Yubo Fan, Ipek Oguz, Benoit M. Dawant. 109-118 [doi]
- Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma SegmentationMuhammad Irfan Khan, Mohammad Ayyaz Azeem, Esa Alhoniemi, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi. 121-132 [doi]
- A Local Score Strategy for Weight Aggregation in Federated LearningGaurav Singh. 133-141 [doi]
- Ensemble Outperforms Single Models in Brain Tumor SegmentationJianxun Ren, Wei Zhang, Ning An, Qingyu Hu, Youjia Zhang, Ying Zhou. 142-153 [doi]
- FeTS Challenge 2022 Task 1: Implementing FedMGDA + and a New PartitioningVasilis Siomos, Giacomo Tarroni, Jonathan Passerat-Palmbach. 154-160 [doi]
- Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client PruningMeirui Jiang, Hongzheng Yang, Xiaofan Zhang, Shaoting Zhang 0001, Qi Dou 0001. 161-172 [doi]
- Hybrid Window Attention Based Transformer Architecture for Brain Tumor SegmentationHimashi Peiris, Munawar Hayat, Zhaolin Chen, Gary F. Egan, Mehrtash Harandi. 173-182 [doi]
- Robust Learning Protocol for Federated Tumor Segmentation ChallengeAmbrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin. 183-195 [doi]
- Model Aggregation for Federated Learning Considering Non-IID and Imbalanced Data DistributionYuan Wang, Renuga Kanagavelu, Qingsong Wei, Yechao Yang, Yong Liu. 196-208 [doi]
- FedPIDAvg: A PID Controller Inspired Aggregation Method for Federated LearningLeon Mächler, Ivan Ezhov, Suprosanna Shit, Johannes C. Paetzold. 209-217 [doi]
- Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor SegmentationKrzysztof Kotowski, Szymon Adamski, Bartosz Machura, Wojciech Malara, Lukasz Zarudzki, Jakub Nalepa. 218-227 [doi]
- Experimenting FedML and NVFLARE for Federated Tumor Segmentation ChallengeYaying Shi, Hongjian Gao, Salman Avestimehr, Yonghong Yan 0001. 228-240 [doi]