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Titlebook: Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries; 8th International Wo Spyridon Bakas,Alessandro Crimi,Reuben Dor

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期刊全称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries
期刊简称8th International Wo
影响因子2023Spyridon Bakas,Alessandro Crimi,Reuben Dorent
视频video
学科分类Lecture Notes in Computer Science
图书封面Titlebook: Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries; 8th International Wo Spyridon Bakas,Alessandro Crimi,Reuben Dor
影响因子This book 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. .
Pindex Conference proceedings 2023
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Unsupervised Anomaly Localization with Structural Feature-Autoencodersaining. Most commonly, the anomaly detection model generates a “normal” version of an input image, and the pixel-wise .-difference of the two is used to localize anomalies. However, large residuals often occur due to imperfect reconstruction of the complex anatomical structures present in most medic
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Transformer Based Models for Unsupervised Anomaly Segmentation in Brain MR Imagesecursor to both diagnostic and therapeutic procedures. Advances in machine learning (ML) aim to increase diagnostic efficiency by replacing a single application with generalized algorithms. The goal of unsupervised anomaly detection (UAD) is to identify potential anomalous regions unseen during trai
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Weighting Schemes for Federated Learning in Heterogeneous and Imbalanced Segmentation Datasetsodel weights. Two central problems arise when sending the updated weights to the central node in a federation: the imbalance of the datasets and data heterogeneity caused by differences in scanners or acquisition protocols. In this paper, we benchmark the federated average algorithm and adapt two we
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Probabilistic Tissue Mapping for Tumor Segmentation and Infiltration Detection of Gliomand-truth manual delineations, one could argue that the binary nature of these labels does not properly reflect the underlying biology, nor does it account for uncertainties in the predicted segmentations. Moreover, the tumor infiltration beyond the contrast-enhanced lesion – visually imperceptible o
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Semi-supervised Intracranial Aneurysm Segmentation with Selected Unlabeled Dataf up to one-third. Therefore, the diagnosis of intracranial aneurysms is of great significance. The widespread use of advanced imaging techniques, such as computed tomography angiography (CTA) and magnetic resonance angiography (MRA), has made it possible to diagnose intracranial aneurysms at an ear
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