標(biāo)題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 4th International Wo Alessandro Crimi,Spyridon Bakas,Theo van [打印本頁(yè)] 作者: Monroe 時(shí)間: 2025-3-21 19:56
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries影響因子(影響力)
書(shū)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(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡(luò)公開(kāi)度
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次學(xué)科排名
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries年度引用學(xué)科排名
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋
書(shū)目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries讀者反饋學(xué)科排名
作者: 耐寒 時(shí)間: 2025-3-21 20:47
Segmenting Brain Tumors from MRI Using Cascaded Multi-modal U-Netse, developing automated techniques for reproducible detection and segmentation of brain tumors from magnetic resonance imaging is a vital research topic. In this paper, we present a deep learning-powered approach for brain tumor segmentation which exploits multiple magnetic-resonance modalities and 作者: overshadow 時(shí)間: 2025-3-22 01:39
Automatic Brain Tumor Segmentation by Exploring the Multi-modality Complementary Information and Casal networks (CNNs) have been widely used for this task. Most of the existing methods integrate the multi-modality information by merging them as multiple channels at the input of the network. However, explicitly exploring the complementary information among different modalities has not been well stu作者: 阻礙 時(shí)間: 2025-3-22 05:12
Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal ns and image intensities of various tumors types. This paper presents a fully automated and efficient brain tumor segmentation method based on 2D Deep Convolutional Neural Networks (DNNs) which automatically extracts the whole tumor and intra-tumor regions, including enhancing tumor, edema and necro作者: PLAYS 時(shí)間: 2025-3-22 11:52 作者: 無(wú)所不知 時(shí)間: 2025-3-22 14:29
Automatic Brain Tumor Segmentation Using Convolutional Neural Networks with Test-Time Augmentational neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of data augmentation at test time, in addition to 作者: 津貼 時(shí)間: 2025-3-22 20:53 作者: chronicle 時(shí)間: 2025-3-23 01:06 作者: medieval 時(shí)間: 2025-3-23 03:51 作者: 比喻好 時(shí)間: 2025-3-23 08:57
A Pretrained DenseNet Encoder for Brain Tumor Segmentationecture. We evaluate the use of a densely connected convolutional network encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two network architectures that can take into account multiple 3D images as inputs. This work aims to identify if a generic pretrained network can be us作者: 小畫(huà)像 時(shí)間: 2025-3-23 12:29
Hierarchical Multi-class Segmentation of Glioma Images Using Networks with Multi-level Activation Funt/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes ‘whole tumor’, ‘tumor core’, ‘a(chǎn)ctive tumor’, the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network co作者: 考古學(xué) 時(shí)間: 2025-3-23 14:31
Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1?mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction,作者: GILD 時(shí)間: 2025-3-23 19:39
Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clinince between subjects with tumor and healthy subjects. In this paper, we exploit a convolutional neural network (CNN) with hypercolumn technique to segment tumor from healthy brain tissue. Hypercolumn is the concatenation of a set of vectors which form by extracting convolutional features from multip作者: DRILL 時(shí)間: 2025-3-23 23:01 作者: seroma 時(shí)間: 2025-3-24 04:58 作者: Innocence 時(shí)間: 2025-3-24 07:02 作者: 決定性 時(shí)間: 2025-3-24 10:58 作者: Obloquy 時(shí)間: 2025-3-24 16:04 作者: PALMY 時(shí)間: 2025-3-24 20:30
Segmentation of Brain Tumors and Patient Survival Prediction: Methods for the BraTS 2018 Challengelocated tumor into tumor core, enhanced tumor, and peritumoral edema..The survival prediction of the patients is done with a rather simple, yet accurate algorithm which outperformed other tested approaches on the train set when thoroughly cross-validated. This finding is consistent with our performa作者: 刪減 時(shí)間: 2025-3-25 02:12
Automatic Brain Tumor Segmentation by Exploring the Multi-modality Complementary Information and Casatial resolution and the number of parameters is only 0.5M. In the BraTS 2018 segmentation task, experiments with the validation dataset show that the proposed method helps to improve the brain tumor segmentation accuracy compared with the common merging strategy. The mean Dice scores on the validat作者: Obstacle 時(shí)間: 2025-3-25 04:09
Deep Convolutional Neural Networks Using U-Net for Automatic Brain Tumor Segmentation in Multimodal ation datasets, which include a total of 351 multimodal MRI volumes of different patients with HGG and LGG tumors representing different shapes, giving promising and objective results close to manual segmentation performances obtained by experienced neuro-radiologists. On the challenge validation da作者: 愉快么 時(shí)間: 2025-3-25 08:44 作者: 繁榮中國(guó) 時(shí)間: 2025-3-25 12:27 作者: Ccu106 時(shí)間: 2025-3-25 17:33
Multi-planar Spatial-ConvNet for Segmentation and Survival Prediction in Brain Cancer the decoder network localize and recover the object details more effectively. These connections allow the network to simultaneously incorporate high-level features along with pixel-level details. A new aggregated loss function helps in effectively handling data imbalance. The integrated segmentatio作者: moratorium 時(shí)間: 2025-3-26 00:00 作者: legislate 時(shí)間: 2025-3-26 04:02 作者: 凹室 時(shí)間: 2025-3-26 06:42 作者: 帶傷害 時(shí)間: 2025-3-26 10:23
Glioma Prognosis: Segmentation of the Tumor and Survival Prediction Using Shape, Geometric and Clini regression on the extracted features using an artificial neural network (ANN). Our model achieves a mean dice score of 89.78%, 82.53% and 76.54% for the whole tumor, tumor core and enhancing tumor respectively in segmentation task and 67.9% in overall survival prediction task with the validation se作者: diskitis 時(shí)間: 2025-3-26 14:26 作者: Incorruptible 時(shí)間: 2025-3-26 19:30 作者: TRUST 時(shí)間: 2025-3-26 21:34
Glioma Segmentation with Cascaded UNetto the 3D UNet architecture and augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data. We evaluate presented approach on BraTS 2018 datas作者: 真實(shí)的你 時(shí)間: 2025-3-27 01:55
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries4th International Wo作者: Conflagration 時(shí)間: 2025-3-27 06:58
0302-9743 in tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges..978-3-030-11725-2978-3-030-11726-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 細(xì)查 時(shí)間: 2025-3-27 10:25 作者: 積習(xí)已深 時(shí)間: 2025-3-27 16:33 作者: Ankylo- 時(shí)間: 2025-3-27 21:16
https://doi.org/10.1007/978-981-33-6133-1ation datasets, which include a total of 351 multimodal MRI volumes of different patients with HGG and LGG tumors representing different shapes, giving promising and objective results close to manual segmentation performances obtained by experienced neuro-radiologists. On the challenge validation da作者: CHART 時(shí)間: 2025-3-27 23:33
C. G. Varelas,A. J. Dualeh,C. A. Steiner and enhancing tumor. Experimental results on BraTS 2018 online validation set achieve average Dice scores of 0.9048, 0.8364 and 0.7748 for whole tumor, tumor core and enhancing tumor, respectively. The corresponding values for BraTS 2018 online testing set are 0.8761, 0.7953 and 0.7364, respectivel作者: 送秋波 時(shí)間: 2025-3-28 04:41
Patrick Hubert,Edith DellacherieS), our method ranks at second place and 5th place out of 60+ participating teams on survival prediction task and segmentation task respectively, achieving a promising 61.0% accuracy on classification of long-survivors, mid-survivors and short-survivors.作者: bioavailability 時(shí)間: 2025-3-28 06:17 作者: 填料 時(shí)間: 2025-3-28 11:18
https://doi.org/10.1007/978-3-642-61036-3 distance of 2.9?mm, 3.95?mm, and 6.48?mm for enhanced tumor core, whole tumor and tumor core respectively on the validation set. This scores degrades to 0.77, 0.88, 0.78 and 95% Hausdorff distance of 3.6?mm, 5.72?mm, and 5.83?mm on the testing set [.].作者: 惹人反感 時(shí)間: 2025-3-28 15:02
https://doi.org/10.1007/978-3-642-61036-3ning dataset of Brats2018, and 20% of the dataset is regarded as the validation dataset to determine parameters. When the parameters are fixed, we retrain the model on the whole training dataset. The performance achieved on the validation leaderboard is 86%, 77% and 72% Dice scores for the whole tum作者: JADED 時(shí)間: 2025-3-28 19:05
Jaime Prilusky,Eran Hodis,Joel L. Sussmanal of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.作者: 心痛 時(shí)間: 2025-3-29 00:37 作者: 較早 時(shí)間: 2025-3-29 06:11 作者: beta-carotene 時(shí)間: 2025-3-29 07:22 作者: Congeal 時(shí)間: 2025-3-29 14:05
Johan Fransson,Carl A.K. Borrebaeckto the 3D UNet architecture and augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data. We evaluate presented approach on BraTS 2018 datas作者: Vulvodynia 時(shí)間: 2025-3-29 18:03
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/190323.jpg作者: Pigeon 時(shí)間: 2025-3-29 20:07
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries978-3-030-11726-9Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Keratectomy 時(shí)間: 2025-3-30 01:52 作者: 引導(dǎo) 時(shí)間: 2025-3-30 04:59 作者: novelty 時(shí)間: 2025-3-30 10:37
Challenges in Mature Field Redevelopment, a possible resection of the tumor. Hence, an automatic segmentation algorithm would be preferable, as it does not suffer from inter-rater variability. On top, results could be available immediately after the brain imaging procedure. Using this automatic tumor segmentation, it could also be possibl作者: Exposure 時(shí)間: 2025-3-30 13:07 作者: Colonoscopy 時(shí)間: 2025-3-30 17:57 作者: Mettle 時(shí)間: 2025-3-30 22:32
https://doi.org/10.1007/978-981-33-6133-1ns and image intensities of various tumors types. This paper presents a fully automated and efficient brain tumor segmentation method based on 2D Deep Convolutional Neural Networks (DNNs) which automatically extracts the whole tumor and intra-tumor regions, including enhancing tumor, edema and necro作者: 背景 時(shí)間: 2025-3-31 04:52 作者: 輕浮女 時(shí)間: 2025-3-31 08:38 作者: Cardioversion 時(shí)間: 2025-3-31 09:12
Y.-X. Zhang,F. S. Hwang,T. E. Hogen-Eschtion have been replaced by 3D convolutions. The key differences between the architectures are the size of the receptive field and the number of feature maps on the final layers. The obtained results are comparable to the top methods of previous Brats Challenges when median is use to average the resu作者: obscurity 時(shí)間: 2025-3-31 14:25
Patrick Hubert,Edith Dellacherierall survival are important for diagnosis, treatment planning and risk factor characterization. Here we present a deep learning-based framework for brain tumor segmentation and survival prediction in glioma using multimodal MRI scans. For tumor segmentation, we use ensembles of three different 3D CN作者: Judicious 時(shí)間: 2025-3-31 18:34 作者: 滔滔不絕地講 時(shí)間: 2025-3-31 23:27 作者: Cocker 時(shí)間: 2025-4-1 02:23
https://doi.org/10.1007/978-3-642-61036-3nt/nesting is a typical inter-class geometric relationship. In the MICCAI Brain tumor segmentation challenge, with its three hierarchically nested classes ‘whole tumor’, ‘tumor core’, ‘a(chǎn)ctive tumor’, the nested classes relationship is introduced into the 3D-residual-Unet architecture. The network co作者: 惡臭 時(shí)間: 2025-4-1 09:37
Jaime Prilusky,Eran Hodis,Joel L. Sussman For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1?mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction,
