Covenant
发表于 2025-3-21 18:34:54
书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0190329<br><br> <br><br>书目名称Brainlesion:Glioma, Multiple Sclerosis, Strokeand Traumatic Brain Injuries读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0190329<br><br> <br><br>
债务
发表于 2025-3-21 21:05:51
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
Immortal
发表于 2025-3-22 03:21:18
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
拒绝
发表于 2025-3-22 07:52:31
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
软弱
发表于 2025-3-22 09:38:00
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百科全书
发表于 2025-3-22 13:34:26
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羊栏
发表于 2025-3-22 20:15:23
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
标准
发表于 2025-3-22 22:07:49
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Resection
发表于 2025-3-23 02:22:07
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INERT
发表于 2025-3-23 08:57:18
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