Applying Quadratic Penalty Method for Intensity-based Deformable Image Registration on BraTS-Reg Challenge 2022 -- WSSAMNet: Weakly Supervised Semantic Attentive Medical Image Registration Network -- Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients -- 3D Inception-Based TransMorph: Pre- and Post-operative Multi-contrast MRI Registration in Brain Tumors -- Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning -- Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation -- MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation -- An Unpaired Cross-modality Segmentation Framework Using Data Augmentation and Hybrid Convolutional Networks for Segmenting Vestibular Schwannoma and Cochlea.-Weakly Unsupervised Domain Adaptation for Vestibular Schwannoma Segmentation -- Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation -- Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation -- Regularized Weight Aggregation in Networked Federated Learning for Glioblastoma Segmentation -- A Local Score Strategy for Weight Aggregation in Federated Learning -- Ensemble Outperforms Single Models in Brain Tumor Segmentation -- FeTS Challenge 2022 Task 1: Implementing FedMGDA+ and a new partitioning -- Efficient Federated Tumor Segmentation via Parameter Distance Weighted Aggregation and Client Pruning -- Hybrid Window Attention Based Transformer Architecture for Brain Tumor Segmentation -- Robust Learning Protocol for Federated Tumor Segmentation Challenge -- Model Aggregation for Federated Learning Considering Non-IID andImbalanced Data Distribution -- FedPIDAvg: A PID controller inspired aggregation method for Federated Learning -- Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor Segmentation -- Experimenting FedML and NVFLARE for Federated Tumor Segmentation Challenge.