Fixate
发表于 2025-3-21 16:39:31
书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影响因子(影响力)<br> http://impactfactor.cn/if/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影响因子(影响力)学科排名<br> http://impactfactor.cn/ifr/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries网络公开度<br> http://impactfactor.cn/at/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries网络公开度学科排名<br> http://impactfactor.cn/atr/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引频次<br> http://impactfactor.cn/tc/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引频次学科排名<br> http://impactfactor.cn/tcr/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用<br> http://impactfactor.cn/ii/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用学科排名<br> http://impactfactor.cn/iir/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries读者反馈<br> http://impactfactor.cn/5y/?ISSN=BK0190321<br><br> <br><br>书目名称Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries读者反馈学科排名<br> http://impactfactor.cn/5yr/?ISSN=BK0190321<br><br> <br><br>
大厅
发表于 2025-3-21 20:35:35
Deep Convolutional Neural Networks for the Segmentation of Gliomas in Multi-sequence MRIs and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.
badinage
发表于 2025-3-22 01:31:05
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Cytology
发表于 2025-3-22 04:36:50
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fledged
发表于 2025-3-22 10:44:44
Conference proceedings 2016ce on Conferenceon Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015...The 25papers presented in this volume were carefully reviewed and selected from 28submissions. They are grouped around the following topics: brain lesion imageanalysis; brain tumor image segmentation; ischemic stroke lesion imagesegmentation..
Paraplegia
发表于 2025-3-22 15:11:59
Macroevolution in Human Prehistory segmentation and exclude voxels labeled as CSF, ventricles and hemorrhagic lesion and then automatically detect the lesion load. Preliminary results demonstrate that our method is coherent with expert opinion in the identification of lesions. We outline the challenges posed in automatic analysis for TBI.
PLAYS
发表于 2025-3-22 19:56:04
https://doi.org/10.1057/9780230604315s and Low Grade Gliomas we trained two different architectures, one for each grade. Using the proposed method it was possible to obtain promising results in the 2015 Multimodal Brain Tumor Segmentation (BraTS) data set, as well as the second position in the on-site challenge.
傀儡
发表于 2025-3-23 00:40:21
Wolfgang Schönfeld,Stjepan Mutakhat parameter learning leads to comparable or even improved performance. In addition, we also performed experiments to study the impact of the composition of training data on the final segmentation performance. We found that models trained on mixed data sets achieve reasonable performance compared to models trained on stratified data.
FER
发表于 2025-3-23 02:21:47
Rituparna Bose,Alexander J. Bartholomewal features, which have the benefit of no computational overhead and easy extraction from the MR images. On MR images of 18 patients with multiple sclerosis the proposed method achieved the median Dice similarity of 0.73, sensitivity of 0.90 and positive predictive value of 0.61, which indicate accurate segmentation of white-matter lesions.
GAVEL
发表于 2025-3-23 08:54:15
Principle Of Social Subsistenceases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.