LEADER 06047nam 22008055 450 001 9910831012003321 005 20240204174425.0 010 $a3-031-44153-2 024 7 $a10.1007/978-3-031-44153-0 035 $a(CKB)30313905200041 035 $a(MiAaPQ)EBC31106877 035 $a(Au-PeEL)EBL31106877 035 $a(MiAaPQ)EBC31132608 035 $a(Au-PeEL)EBL31132608 035 $a(DE-He213)978-3-031-44153-0 035 $a(EXLCZ)9930313905200041 100 $a20240204d2023 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBrainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries$b[electronic resource] $e8th International Workshop, BrainLes 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Revised Selected Papers, Part II /$fedited by Spyridon Bakas, Alessandro Crimi, Ujjwal Baid, Sylwia Malec, Monika Pytlarz, Bhakti Baheti, Maximilian Zenk, Reuben Dorent 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (256 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14092 311 08$a9783031441523 327 $aApplying 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 and Imbalanced 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. 330 $aThis two volume-set LNCS 13769 and LNCS 14092 constitutes the refereed proceedings of the 8th International MICCAI Brainlesion Workshop, BrainLes 2022, as well as the Brain Tumor Segmentation (BraTS) Challenge, the Brain Tumor Sequence Registration (BraTS-Reg) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the Federated Tumor Segmentation (FeTS) Challenge. These were held jointly at the Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2022, in September 2022. The 46 revised full papers presented in these volumes were selected form 65 submissions. The presented contributions describe the research of computational scientists and clinical researchers working on brain lesions - specifically glioma, multiple sclerosis, cerebral stroke, traumatic brain injuries, vestibular schwannoma, and white matter hyper-intensities of presumed vascular origin. . 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14092 606 $aComputer vision 606 $aMedical informatics 606 $aSocial sciences$xData processing 606 $aApplication software 606 $aEducation$xData processing 606 $aArtificial intelligence 606 $aComputer Vision 606 $aHealth Informatics 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputer and Information Systems Applications 606 $aComputers and Education 606 $aArtificial Intelligence 615 0$aComputer vision. 615 0$aMedical informatics. 615 0$aSocial sciences$xData processing. 615 0$aApplication software. 615 0$aEducation$xData processing. 615 0$aArtificial intelligence. 615 14$aComputer Vision. 615 24$aHealth Informatics. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputer and Information Systems Applications. 615 24$aComputers and Education. 615 24$aArtificial Intelligence. 676 $a616.8 700 $aBakas$b Spyridon$01631607 701 $aCrimi$b Alessandro$01354885 701 $aBaid$b Ujjwal$01631608 701 $aMalec$b Sylwia$01631609 701 $aPytlarz$b Monika$01631610 701 $aBaheti$b Bhakti$01631611 701 $aZenk$b Maximilian$01631612 701 $aDorent$b Reuben$01631613 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831012003321 997 $aUNINA