標(biāo)題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 5th International Wo Alessandro Crimi,Spyridon Bakas Conferen [打印本頁(yè)] 作者: Localized 時(shí)間: 2025-3-21 18:33
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)學(xué)科排名
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書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次
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書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋學(xué)科排名
作者: PATHY 時(shí)間: 2025-3-21 22:12
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluationand evaluated the best model in a radiation therapy department for three types of brain tumors: meningiomas, schwannomas and multiple brain metastases. The developed semiautomatic segmentation system accelerates the contouring process by 2.2 times on average and increases inter-rater agreement from 92.0% to ..作者: 束縛 時(shí)間: 2025-3-22 02:21
The Fundamental Macroeconomic Identitiesoss function during the learning phase. Even though this approach leads to improved Dice scores, we find that, both theoretically and empirically on four medical tasks, it can introduce a volumetric bias for tasks with high inherent uncertainty. As such, this may limit the method’s clinical applicability.作者: Pudendal-Nerve 時(shí)間: 2025-3-22 07:46 作者: 教義 時(shí)間: 2025-3-22 08:49
Conference proceedings 20202 selected papers from 32 submissions); brain tumor image segmentation (57 selected papers from 102 submissions); combined MRI and pathology brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).作者: condemn 時(shí)間: 2025-3-22 16:44
Conference proceedings 2020s 2019, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge, as well as the tutorial session on Tools Allowing Clinical Translation of Image Computing Algori作者: NAUT 時(shí)間: 2025-3-22 17:11 作者: 使服水土 時(shí)間: 2025-3-22 22:42 作者: CANE 時(shí)間: 2025-3-23 03:37
TBI Lesion Segmentation in Head CT: Impact of Preprocessing and Data Augmentationtion yields improved performance, skull-stripping can be replaced by using the right intensity window, and affine-to-atlas registration is not necessary if we use sufficient spatial augmentation. Since both skull-stripping and affine-to-atlas registration are susceptible to failure, we recommend the作者: LINE 時(shí)間: 2025-3-23 08:10
Aneurysm Identification in Cerebral Models with Multiview Convolutional Neural Networketermined in the task. We have applied the labeling task on 56 3D mesh models with aneurysms (positive) and 65 models without aneurysms (negative). The average accuracy of individual projected images is 87.86%, while that of the model is 93.4% with the best view number. The framework is highly effec作者: 詞匯 時(shí)間: 2025-3-23 13:35 作者: 進(jìn)入 時(shí)間: 2025-3-23 16:57
Towards Population-Based Histologic Stain Normalization of Glioblastomam a large population of anatomically annotated hematoxylin and eosin (.) whole-slide images from the Ivy Glioblastoma Atlas Project (.). Two board-certified neuropathologists manually reviewed and selected annotations in 509 slides, according to the World Health Organization definitions. We computed作者: 食道 時(shí)間: 2025-3-23 18:31 作者: atopic-rhinitis 時(shí)間: 2025-3-24 01:30 作者: FLINT 時(shí)間: 2025-3-24 05:30
Global and Local Multi-scale Feature Fusion Enhancement for Brain Tumor Segmentation and Pancreas Setures, and to fuse feature information at different scales. LMF can integrate local dense multi-scale context features layer by layer in the network, thus improving the ability of network to encode interdependent relationships among boundary pixels. Based on the above two modules, we propose a novel作者: Vldl379 時(shí)間: 2025-3-24 07:12 作者: EVEN 時(shí)間: 2025-3-24 10:42 作者: Nmda-Receptor 時(shí)間: 2025-3-24 17:12
3D U-Net Based Brain Tumor Segmentation and Survival Days Predictionor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.作者: 編輯才信任 時(shí)間: 2025-3-24 19:55 作者: chance 時(shí)間: 2025-3-25 01:57 作者: exquisite 時(shí)間: 2025-3-25 03:23 作者: abstemious 時(shí)間: 2025-3-25 08:56
TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set. The proposed method was ranked among the top in the task of Quantification of Uncertainty in Segmentation.作者: intercede 時(shí)間: 2025-3-25 12:04
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries5th International Wo作者: Tremor 時(shí)間: 2025-3-25 16:08
Obert Nyawata,Clever Mumbengegwi inter-slice and global intra-slice features are jointly exploited to predict class label of the central voxel in a given patch through the 2D CNN classifier. We implicitly apply all modalities through trainable parameters to assign weights to the contributions of each sequence for segmentation. Exp作者: 泥沼 時(shí)間: 2025-3-25 23:25 作者: 頂點(diǎn) 時(shí)間: 2025-3-26 00:13
Obert Nyawata,Clever Mumbengegwietermined in the task. We have applied the labeling task on 56 3D mesh models with aneurysms (positive) and 65 models without aneurysms (negative). The average accuracy of individual projected images is 87.86%, while that of the model is 93.4% with the best view number. The framework is highly effec作者: Charade 時(shí)間: 2025-3-26 07:48 作者: LATE 時(shí)間: 2025-3-26 09:49
Trade and Financial Relations Among Nations,m a large population of anatomically annotated hematoxylin and eosin (.) whole-slide images from the Ivy Glioblastoma Atlas Project (.). Two board-certified neuropathologists manually reviewed and selected annotations in 509 slides, according to the World Health Organization definitions. We computed作者: puzzle 時(shí)間: 2025-3-26 15:57
John Evans-Pritchard B.Sc. Econprint. We have identified a retrospective dataset of 1,796 mpMRI brain tumor scans, with corresponding manually-inspected and verified gold-standard brain tissue segmentations, acquired during standard clinical practice under varying acquisition protocols at the Hospital of the University of Pennsyl作者: Lipoprotein(A) 時(shí)間: 2025-3-26 16:58 作者: 加劇 時(shí)間: 2025-3-27 00:08
Central Banks and Monetary Policytures, and to fuse feature information at different scales. LMF can integrate local dense multi-scale context features layer by layer in the network, thus improving the ability of network to encode interdependent relationships among boundary pixels. Based on the above two modules, we propose a novel作者: cliche 時(shí)間: 2025-3-27 03:53 作者: Negotiate 時(shí)間: 2025-3-27 07:38
The Rise and Fall of the Great Moderationect of the saliency map is evaluated on 2293 patient multi-channel MRI scans acquired during two proprietary, multi-center clinical trials for MS treatments. Inclusion of the attention mechanism results in a decrease in false positive lesion voxels over a basic U-Net?[.] and DeepMedic?[.]. In terms 作者: Euphonious 時(shí)間: 2025-3-27 10:25
Rosalind Leva?i?,Alexander Rebmannor size to brain size and the area of tumor surface as well as age of subjects are extracted from predicted tumor labels and have been used for the overall survival days prediction task. The accuracy could be 0.448 on the validation dataset, and 0.551 on the final test dataset.作者: 聯(lián)想 時(shí)間: 2025-3-27 16:13 作者: EXULT 時(shí)間: 2025-3-27 18:33 作者: Bronchial-Tubes 時(shí)間: 2025-3-27 23:01 作者: Entropion 時(shí)間: 2025-3-28 04:51
https://doi.org/10.1007/978-1-349-23673-2, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set. The proposed method was ranked among the top in the task of Quantification of Uncertainty in Segmentation.作者: 大方不好 時(shí)間: 2025-3-28 07:25
0302-9743 brain tumor classification (4 selected papers from 5 submissions); tools allowing clinical translation of image computing algorithms (2 selected papers from 3 submissions.).978-3-030-46639-8978-3-030-46640-4Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 完整 時(shí)間: 2025-3-28 14:06
Convolutional 3D to 2D Patch Conversion for Pixel-Wise Glioma Segmentation in?MRI Scansors is a challenging task for computer-aided diagnosis due to low-tissue contrast in the tumor subregions. To overcome this, we devise a novel pixel-wise segmentation framework through a convolutional 3D to 2D MR patch conversion model to predict class labels of the central pixel in the input slidin作者: forestry 時(shí)間: 2025-3-28 18:34 作者: affluent 時(shí)間: 2025-3-28 20:47 作者: 原始 時(shí)間: 2025-3-29 00:35 作者: Console 時(shí)間: 2025-3-29 05:37 作者: Limited 時(shí)間: 2025-3-29 08:49
Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learningthods have primarily targeted non-pathologically-affected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI作者: Ardent 時(shí)間: 2025-3-29 13:51
Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomesetworks for the prediction of the follow-up tissue outcome in strokes are, however, not yet accurate enough or capable of properly modeling the growth mechanisms of ischaemic stroke..In our previous shape space interpolation approach, the prediction of the follow-up lesion shape has been bounded usi作者: Orthodontics 時(shí)間: 2025-3-29 17:59 作者: 挫敗 時(shí)間: 2025-3-29 20:45
Optimization with Soft Dice Can Lead to a Volumetric Biaspare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using metrics such as the Dice score. Recent segmentation methods based on convolutional neural networks use a differentiable surrogate of the Dice score, such as soft Dice, explicitly as the l作者: ETHER 時(shí)間: 2025-3-30 00:42 作者: 泥瓦匠 時(shí)間: 2025-3-30 04:59 作者: 高深莫測(cè) 時(shí)間: 2025-3-30 12:09
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluationatment, accurate delineation of brain tumors is of great importance. However, slice-by-slice manual segmentation on T1c MRI could be time-consuming (especially for multiple metastases) and subjective. In our work, we compared several deep convolutional networks architectures and training procedures 作者: Annotate 時(shí)間: 2025-3-30 12:53
3D U-Net Based Brain Tumor Segmentation and Survival Days Predictioneatment of brain tumors requires an accurate and reliable segmentation of brain tumors as a prerequisite. However, such work conventionally requires brain surgeons significant amount of time. Computer vision techniques could provide surgeons a relief from the tedious marking procedure. In this paper作者: 詢問 時(shí)間: 2025-3-30 17:11 作者: Delirium 時(shí)間: 2025-3-30 20:57 作者: Odyssey 時(shí)間: 2025-3-31 03:26 作者: conquer 時(shí)間: 2025-3-31 06:53
TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networksa high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-t
