標(biāo)題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 6th International Wo Alessandro Crimi,Spyridon Bakas Conferen [打印本頁(yè)] 作者: 遠(yuǎn)見(jiàn) 時(shí)間: 2025-3-21 18:36
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
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡(luò)公開度
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
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次學(xué)科排名
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用學(xué)科排名
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: 辭職 時(shí)間: 2025-3-21 23:32 作者: Jubilation 時(shí)間: 2025-3-22 02:10
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries978-3-030-72087-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: semiskilled 時(shí)間: 2025-3-22 08:13 作者: 腐爛 時(shí)間: 2025-3-22 09:32
Lois T. Hunt,Margaret O. Dayhoffccurate treatment planning is key. Magnetic resonance imaging (MRI) is a widely used imaging technique for the assessment of these tumours but the large amount of data generated by them prevents rapid manual segmentation, the task of dividing visual input into tumorous and non-tumorous regions. Henc作者: Dysplasia 時(shí)間: 2025-3-22 14:35 作者: handle 時(shí)間: 2025-3-22 18:56
https://doi.org/10.1007/978-3-642-60986-2 methods by implementing the U-Net model and trialing various modifications to the training and inference strategies. The trials were performed and tested on the Multimodal Brain Tumor Segmentation dataset that provides MR images of brain tumors along with manual segmentations for hundreds of subjec作者: 蒸發(fā) 時(shí)間: 2025-3-22 23:10
Polymer-Metal Complexes in Living Systems, MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional 作者: Obvious 時(shí)間: 2025-3-23 02:41
https://doi.org/10.1007/978-1-4899-2809-2et uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that t作者: 意外 時(shí)間: 2025-3-23 07:29
Radiation-Activated Polymerizationn tumour segmentation may be more compatible with current MRI acquisition protocols than 3D methods because clinical MRI is most commonly a slice-based modality. A 2D Dense-UNet segmentation model was trained on the BraTS 2020 dataset. Mean Dice values achieved on the test dataset were: 0.859 (WT), 作者: padding 時(shí)間: 2025-3-23 11:15 作者: 有組織 時(shí)間: 2025-3-23 13:53
Compounding and Processing of Plastics remains an open question. The key is to effectively model spatial-temporal information that resides in the input volumetric data. In this paper, we propose Multi-View Pointwise U-Net (MVP U-Net) for brain tumor segmentation. Our segmentation approach follows encoder-decoder based 3D U-Net architect作者: penance 時(shí)間: 2025-3-23 19:36
Compounding and Processing of Plasticsprocessing steps were applied before training, such as intensity normalization, high intensity cutting, cropping, and random flips. 2D and 3D solutions are implemented and tested, and results show that the 3D network outperforms 2D directions, therefore we stayed with 3D directions..The novelty of t作者: eulogize 時(shí)間: 2025-3-24 01:59 作者: 老人病學(xué) 時(shí)間: 2025-3-24 06:07 作者: vitrectomy 時(shí)間: 2025-3-24 09:58 作者: progestogen 時(shí)間: 2025-3-24 13:39
Macromolecular Change and the Synapsee large number of magnetic resonance images (MRIs). In order to make full use of small dataset like BraTS 2020, we propose a deep supervision-based 2D residual U-net for efficient and automatic brain tumor segmentation. In our network, residual blocks are used to alleviate the gradient dispersion ca作者: 大雨 時(shí)間: 2025-3-24 15:43 作者: PURG 時(shí)間: 2025-3-24 22:37
https://doi.org/10.1007/978-1-4684-6042-1tation from Magnetic Resonance Images. The architecture consists of a cascade of three Deep Layer Aggregation neural networks, where each stage elaborates the response using the feature maps and the probabilities of the previous stage, and the MRI channels as inputs. The neuroimaging data are part o作者: aggrieve 時(shí)間: 2025-3-25 02:32
https://doi.org/10.1007/978-1-4684-6042-1al Neural Network (2D-CNN) and its 3D variant, known as 3D-CNN based architectures, have been proposed in previous studies, which are used to capture contextual information. The 3D models capture depth information, making them an automatic choice for glioma segmentation from 3D MRI images. However, 作者: CRUE 時(shí)間: 2025-3-25 06:50
Larry R. Squire,Samuel H. Barondesion, the population loss function, and the optimizer. However, methods developed to compete in recent BraTS challenges tend to focus only on the design of deep neural network architectures, while paying less attention to the three other aspects. In this paper, we experimented with adopting the oppos作者: 取消 時(shí)間: 2025-3-25 10:37 作者: Transfusion 時(shí)間: 2025-3-25 12:58
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries6th International Wo作者: 合乎習(xí)俗 時(shí)間: 2025-3-25 18:47
0302-9743 rom 75 submissions); and computational precision medicine: radiology-pathology challenge on brain tumor classification (6 selected papers from 6 submissions)...*The workshop and challenges were held virtually..978-3-030-72086-5978-3-030-72087-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: jovial 時(shí)間: 2025-3-25 21:55 作者: G-spot 時(shí)間: 2025-3-26 02:05
Jaap J. Beintema,Johannes A. Lenstraand tested on 166 unseen cases from the testing dataset using a blind testing approach. The quantitative and qualitative results demonstrate that our proposed network provides efficient segmentation of brain tumors. The mean Dice overlap measures for automatic brain tumor segmentation of the validat作者: 河流 時(shí)間: 2025-3-26 04:48 作者: 取之不竭 時(shí)間: 2025-3-26 11:54 作者: Biofeedback 時(shí)間: 2025-3-26 12:40
https://doi.org/10.1007/978-1-4899-2809-2in this procedure. Thus, at the pre-processing stage, the proposed framework provided dimension-hybridized intensity feature maps and image sets after the affine transformations simultaneously. Then the feature maps and the transformed images were concatenated and then became the inputs of the atten作者: GILD 時(shí)間: 2025-3-26 17:47
Compounding and Processing of Plasticsd while the number of parameters can be reduced. In BraTS 2020 testing dataset, the mean Dice scores of the proposed method were 0.715, 0.839, and 0.768 for enhanced tumor, whole tumor, and tumor core, respectively. The results show the effectiveness of the proposed MVP U-Net with the SE block for m作者: 光亮 時(shí)間: 2025-3-26 22:11
Compounding and Processing of Plasticsork’s prediction and the raw image features to estimate the posterior distribution (the tumor contour) using energy function minimization..The proposed methods are evaluated within the framework of the BRATS 2020 challenge. Measured on the test dataset the mean dice scores of the whole tumor (WT), t作者: Spirometry 時(shí)間: 2025-3-27 03:04 作者: fringe 時(shí)間: 2025-3-27 06:07 作者: 畸形 時(shí)間: 2025-3-27 12:31
Larry R. Squire,Samuel H. Barondesoss is a per-sample loss function that allows taking advantage of the hierarchical structure of the tumor regions labeled in BraTS. Distributionally robust optimization is a generalization of empirical risk minimization that accounts for the presence of underrepresented subdomains in the training da作者: CLAY 時(shí)間: 2025-3-27 13:48
Efficient Brain Tumour Segmentation Using Co-registered Data and Ensembles of Specialised Learnersrent MRI modalities allow models to specialise on certain labels or regions, which can then be ensembled to achieve improved predictions. These hypotheses were tested by training and evaluating 3D U-Net models on the BraTS 2020 data set. The experiments show that these hypotheses are indeed valid.作者: Cardiac 時(shí)間: 2025-3-27 21:08
Efficient MRI Brain Tumor Segmentation Using Multi-resolution Encoder-Decoder Networksand tested on 166 unseen cases from the testing dataset using a blind testing approach. The quantitative and qualitative results demonstrate that our proposed network provides efficient segmentation of brain tumors. The mean Dice overlap measures for automatic brain tumor segmentation of the validat作者: 躲債 時(shí)間: 2025-3-28 01:12 作者: 愉快么 時(shí)間: 2025-3-28 04:41 作者: Sinus-Node 時(shí)間: 2025-3-28 08:30 作者: 賄賂 時(shí)間: 2025-3-28 12:51
MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentationd while the number of parameters can be reduced. In BraTS 2020 testing dataset, the mean Dice scores of the proposed method were 0.715, 0.839, and 0.768 for enhanced tumor, whole tumor, and tumor core, respectively. The results show the effectiveness of the proposed MVP U-Net with the SE block for m作者: CAMP 時(shí)間: 2025-3-28 14:50
Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-Processingork’s prediction and the raw image features to estimate the posterior distribution (the tumor contour) using energy function minimization..The proposed methods are evaluated within the framework of the BRATS 2020 challenge. Measured on the test dataset the mean dice scores of the whole tumor (WT), t作者: 不妥協(xié) 時(shí)間: 2025-3-28 21:18
Brain Tumor Segmentation and Associated Uncertainty Evaluation Using Multi-sequences MRI Mixture Dat associated uncertainty evaluation performance..Proposed in this paper method also demonstrates strong improvement on the segmentation problem. This conclusion was done with respect to Dice metric, Sensitivity and Specificity compare to identical training/validation procedure based only on any singl作者: Blazon 時(shí)間: 2025-3-28 23:04
Glioma Segmentation Using Ensemble of 2D/3D U-Nets and Survival Prediction Using Multiple Features Fns. In this study, radiomic and image-based features were fused to predict the OS time of patients. Experimental results on BraTS 2020 testing dataset achieved a dice score of 0.79 on Enhancing Tumor (ET), 0.87 on Whole Tumor (WT), and 0.83 on Tumor Core (TC). For OS prediction task, results on BraT作者: 否決 時(shí)間: 2025-3-29 03:07 作者: GROUP 時(shí)間: 2025-3-29 07:40
Conference proceedings 2021es 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervent作者: 歪曲道理 時(shí)間: 2025-3-29 14:34
https://doi.org/10.1007/978-1-4899-2809-226.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor core, respectively. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.作者: defile 時(shí)間: 2025-3-29 18:29 作者: 咆哮 時(shí)間: 2025-3-29 20:20
https://doi.org/10.1007/978-1-4684-6042-1cing tumor, whole tumor and tumor core respectively on the training dataset. The same model gave mean Dice Coefficient of 0.57, 0.73, and 0.61 on the validation dataset and 0.59, 0.72, and 0.57 on the test dataset.作者: assail 時(shí)間: 2025-3-30 00:29
https://doi.org/10.1007/978-1-4684-6042-1t sets. In the Test set, the experimental results achieved a Dice score of 0.8858, 0.8297 and 0.7900, with an Hausdorff Distance of 5.32?mm, 22.32?mm and 20.44?mm for the whole tumor, core tumor and enhanced tumor, respectively.作者: Hormones 時(shí)間: 2025-3-30 07:17
H,NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Chall26.57525, 4.18426, and 4.97162 for the enhancing tumor, whole tumor, and tumor core, respectively. Our method won the second place in the BraTS 2020 challenge segmentation task out of nearly 80 participants.作者: 馬具 時(shí)間: 2025-3-30 08:49
A Deep Supervision CNN Network for Brain Tumor Segmentationing stability and enable the encoder to extract richer visual features. The CBICA’s IPP’s evaluation of the segmentation results verifies the effectiveness of our method. The average Dice of ET, WT and TC are 0.7593, 0.8726 and 0.7879 respectively.作者: GENRE 時(shí)間: 2025-3-30 14:39 作者: 溫和女孩 時(shí)間: 2025-3-30 19:48 作者: Platelet 時(shí)間: 2025-3-30 22:39
0302-9743 op, BrainLes 2020, the International Multimodal Brain Tumor Segmentation (BraTS) challenge, and the Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification (CPM-RadPath) challenge. These were held jointly at the 23rd Medical Image Computing for Computer Assisted作者: filial 時(shí)間: 2025-3-31 02:47 作者: 歪曲道理 時(shí)間: 2025-3-31 07:59
Lightweight U-Nets for Brain Tumor Segmentationtiple Skinny networks over all image planes (axial, coronal, and sagittal), and form an ensemble containing such models. The experiments showed that our approach allows us to obtain accurate brain tumor delineation from multi-modal magnetic resonance images.作者: 原始 時(shí)間: 2025-3-31 10:36 作者: 勾引 時(shí)間: 2025-3-31 13:26
