Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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