標(biāo)題: Titlebook: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries; 7th International Wo Alessandro Crimi,Spyridon Bakas Conferen [打印本頁(yè)] 作者: 分類 時(shí)間: 2025-3-21 17:46
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讀者反饋學(xué)科排名
作者: Radiculopathy 時(shí)間: 2025-3-22 00:17 作者: Distribution 時(shí)間: 2025-3-22 03:52
MS UNet: Multi-scale 3D UNet for?Brain Tumor Segmentation 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates multi-scale features from image data. The multi-scaled features play an essential role in brain tumor segmentation. Researchers presented numerous multi-scal作者: deactivate 時(shí)間: 2025-3-22 05:41 作者: Ringworm 時(shí)間: 2025-3-22 12:34
Orthogonal-Nets: A Large Ensemble of 2D Neural Networks for 3D Brain Tumor Segmentation slices of the image from axial, sagittal, and coronal views of the 3D brain volume and predicts the probability for the tumor segmentation region. The predicted probability distributions from all three views are averaged to generate a 3D probability distribution map that is subsequently used to pre作者: 藐視 時(shí)間: 2025-3-22 15:21
Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentationeam’s solution (open brats2020, ranked among the top ten teams work), we proposed a similar as 3D U-Net neural network, called as TE U-Net, to differentiate glioma sub-regions class. According that automatically learns to focus on sub-regions class structures of varying shapes and sizes, we proposed作者: enmesh 時(shí)間: 2025-3-22 17:11 作者: START 時(shí)間: 2025-3-23 00:39
Brain Tumor Segmentation with Patch-Based 3D Attention UNet from Multi-parametric MRIiparametric MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness作者: mastopexy 時(shí)間: 2025-3-23 05:07
Dice Focal Loss with?ResNet-like Encoder-Decoder Architecture in?3D Brain Tumor Segmentationtment planning, image-guided interventions, monitoring tumor growth, and the generation of radiotherapy maps. However, manual delineation practices has suffered from many problems such as requiring anatomical knowledge, taking considerable time for annotation, showing inaccuracy due to human error. 作者: Liberate 時(shí)間: 2025-3-23 09:25
HNF-Netv2 for?Brain Tumor Segmentation Using Multi-modal MR Imagingn tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated ou作者: JOG 時(shí)間: 2025-3-23 11:25
Disparity Autoencoders for Multi-class Brain Tumor Segmentationluding diagnosis, monitoring, and treatment planning of gliomas. The purpose of this work was to develop a fully automated deep learning framework for multi-class brain tumor segmentation. Brain tumor cases with multi-parametric MR Images from the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Ch作者: overweight 時(shí)間: 2025-3-23 15:44 作者: coalition 時(shí)間: 2025-3-23 20:36
An Ensemble Approach to?Automatic Brain Tumor Segmentationer, track tumor change, and make treatment plans. With the development of machine learning (ML)/Deep Learning (DL) image segmentation methods, the performance of medical image segmentation has significantly improved especially in terms of accuracy and time efficiency. Performance of typical deep lea作者: Additive 時(shí)間: 2025-3-24 01:24 作者: AWL 時(shí)間: 2025-3-24 02:27
Redundancy Reduction in?Semantic Segmentation of?3D Brain Tumor MRIsh of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maint作者: 壯觀的游行 時(shí)間: 2025-3-24 09:40 作者: 粗語(yǔ) 時(shí)間: 2025-3-24 13:01 作者: Endoscope 時(shí)間: 2025-3-24 15:20 作者: Anthology 時(shí)間: 2025-3-24 20:06 作者: palliative-care 時(shí)間: 2025-3-25 01:10
Macroeconomics of Monetary Uniony, we utilize the idea of deep supervision for multiple depths at the decoder. We validate the MS UNet on the BraTS 2021 validation dataset. The dice (DSC) scores of the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) are ., ., and ., respectively.作者: Feedback 時(shí)間: 2025-3-25 06:27
Simultaneous Decisions: Cold-Turkey Policiesrate low-level details with high-level feature maps at different scales. Our framework was trained using the 1251 challenge training cases provided by BraTS 2021, and achieved an average Dice Similarity Coefficient (DSC) of 0.9277, 0.8851 and 0.8754, as well as . Hausdorff distance (in millimeter) o作者: 標(biāo)準(zhǔn) 時(shí)間: 2025-3-25 10:43 作者: 小丑 時(shí)間: 2025-3-25 13:13 作者: 偶然 時(shí)間: 2025-3-25 16:45 作者: Flatter 時(shí)間: 2025-3-25 20:59
https://doi.org/10.1007/978-4-431-55807-1for the validation set, and 0.8769, 0.8721, 0.9266 average dice for the testing set. Our team (NVAUTO) submission was the top performing in terms of ET and TC scores, and using the Brats ranking system (based on the dice and Hausdorff distance ranking per case) achieved the 2nd place on the validati作者: 植物茂盛 時(shí)間: 2025-3-26 03:47
Evaluation of Manufacturing Systemst-time augmentation produces an average dice score of 89.4% and an average Hausdorff 95% distance of 10.0?mm when evaluated on the BraTS 2021 testing dataset. Our code and trained models are publicly available at ..作者: glucagon 時(shí)間: 2025-3-26 04:45
Evaluation of Manufacturing Systems(enhancing tumor), 0.87837 (tumor core), and 0.92723 (whole tumor). Finally, our algorithm allowed us to take the . place (out of 1600 participants) in the BraTS’21 Challenge, with the average Dice score over the test data of 0.86317, 0.87987, and 0.92838 for the enhancing tumor, tumor core and whol作者: DEFT 時(shí)間: 2025-3-26 12:33 作者: 貪心 時(shí)間: 2025-3-26 15:37 作者: 瑪瑙 時(shí)間: 2025-3-26 19:19 作者: Neuropeptides 時(shí)間: 2025-3-26 21:47 作者: 原告 時(shí)間: 2025-3-27 02:19
Disparity Autoencoders for Multi-class Brain Tumor Segmentation and ET respectively on the validation dataset and 0.89, 0.82, 0.81 for WT, TC and ET respectively on the test dataset. This framework could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.作者: 異端邪說(shuō)下 時(shí)間: 2025-3-27 08:08
Redundancy Reduction in?Semantic Segmentation of?3D Brain Tumor MRIsfor the validation set, and 0.8769, 0.8721, 0.9266 average dice for the testing set. Our team (NVAUTO) submission was the top performing in terms of ET and TC scores, and using the Brats ranking system (based on the dice and Hausdorff distance ranking per case) achieved the 2nd place on the validati作者: 漫步 時(shí)間: 2025-3-27 13:16
Generalized Wasserstein Dice Loss, Test-Time Augmentation, and?Transformers for?the?BraTS 2021 Challt-time augmentation produces an average dice score of 89.4% and an average Hausdorff 95% distance of 10.0?mm when evaluated on the BraTS 2021 testing dataset. Our code and trained models are publicly available at ..作者: intuition 時(shí)間: 2025-3-27 16:09 作者: drusen 時(shí)間: 2025-3-27 21:46 作者: Grasping 時(shí)間: 2025-3-27 23:10 作者: Iniquitous 時(shí)間: 2025-3-28 04:22 作者: Dysarthria 時(shí)間: 2025-3-28 08:24 作者: Neutropenia 時(shí)間: 2025-3-28 12:54
Simultaneous Decisions: Cold-Turkey Policiession transformer networks have exhibited exceptional performance in 2D image classification tasks. Compared with CNNs, transformer networks have an appealing advantage of extracting long-range features due to their self-attention algorithm. Therefore, we propose a CNN-Transformer combined model, cal作者: angiography 時(shí)間: 2025-3-28 14:34 作者: 創(chuàng)作 時(shí)間: 2025-3-28 20:20
Macroeconomics of Monetary Union 3D CNN architectures for medical imaging tasks, including brain tumor segmentation. The skip connection in the UNet architecture concatenates multi-scale features from image data. The multi-scaled features play an essential role in brain tumor segmentation. Researchers presented numerous multi-scal作者: 同來(lái)核對(duì) 時(shí)間: 2025-3-29 01:08 作者: 薄膜 時(shí)間: 2025-3-29 06:05
The Countries Differ in Behaviour slices of the image from axial, sagittal, and coronal views of the 3D brain volume and predicts the probability for the tumor segmentation region. The predicted probability distributions from all three views are averaged to generate a 3D probability distribution map that is subsequently used to pre作者: sorbitol 時(shí)間: 2025-3-29 07:41 作者: BOLT 時(shí)間: 2025-3-29 14:56 作者: Foam-Cells 時(shí)間: 2025-3-29 19:29
Does Financial Liberalization Help the Poor?iparametric MRI scans has important clinical relevance in diagnosis, prognosis and treatment of brain tumors. However, due to the highly heterogeneous appearance and shape, segmentation of the sub-regions is very challenging. Recent development using deep learning models has proved its effectiveness作者: 浪費(fèi)物質(zhì) 時(shí)間: 2025-3-29 20:39 作者: 不能平靜 時(shí)間: 2025-3-30 02:16
https://doi.org/10.1057/9780230285583n tumor segmentation using multi-modal MR imaging. In this paper, we extend our HNF-Net to HNF-Netv2 by adding inter-scale and intra-scale semantic discrimination enhancing blocks to further exploit global semantic discrimination for the obtained high-resolution features. We trained and evaluated ou作者: 大溝 時(shí)間: 2025-3-30 05:26
Macroeconomics, Finance and Moneyluding diagnosis, monitoring, and treatment planning of gliomas. The purpose of this work was to develop a fully automated deep learning framework for multi-class brain tumor segmentation. Brain tumor cases with multi-parametric MR Images from the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Ch作者: Cholecystokinin 時(shí)間: 2025-3-30 11:00
https://doi.org/10.1007/978-4-431-55807-1ation (BraTS) Challenge provides a unique opportunity to encourage machine learning solutions to address this challenging task. This year, the 10th edition of BraTS collected a multi-institutional multi-parametric MRI dataset of 2040 cases with typical heterogeneity in large multi-domain imaging dat作者: 預(yù)測(cè) 時(shí)間: 2025-3-30 16:07 作者: reaching 時(shí)間: 2025-3-30 19:48
Advances in Japanese Business and Economicsls with different architectures, we proposed a three-stage model with the quality-aware model ensemble. The first stage locates the tumor with coarse segmentation, while the second stage refines the coarse segmentation in the region of interest. The last stage performs the quality-aware model ensemb作者: incredulity 時(shí)間: 2025-3-30 23:24 作者: 浪蕩子 時(shí)間: 2025-3-31 01:25
A Theoretical Framework of Mixed Systems abundant and high-quality data source to develop automatic algorithms for the task. This paper describes our contribution to the 2021 competition. We developed our methods based on nn-UNet, the winning entry of last year’s competition. We experimented with several modifications, including using a l作者: staging 時(shí)間: 2025-3-31 06:37 作者: 開玩笑 時(shí)間: 2025-3-31 12:26 作者: 感情 時(shí)間: 2025-3-31 13:50 作者: 龍蝦 時(shí)間: 2025-3-31 19:39 作者: farewell 時(shí)間: 2025-4-1 00:31 作者: 阻擋 時(shí)間: 2025-4-1 02:05
https://doi.org/10.1007/978-4-431-55807-1nes. The evaluation of our solution on 570 unseen testing cases resulted in Dice scores of 86.28, 87.12 and 92.10, and Hausdorff distance of 14.36, 17.48 and 5.37 mm for the enhancing tumor, tumor core and whole tumor, respectively.作者: 畏縮 時(shí)間: 2025-4-1 06:11
Advances in Japanese Business and Economics was inspired by Ensembles of Multiple Models and Architectures. In this paper, we train different sub-models separately. Then we train a gating network to credit the inference result from each individual model to get a better result.作者: Magnitude 時(shí)間: 2025-4-1 12:07
Advances in Japanese Business and Economicsvalidation dataset, obtaining an average dice similarity coefficient (DSC) of 0.911, 0.850, 0.816, and average . percentile of Hausdorff distance (HD95) of 4.58, 8.959, 10.400, for whole tumor, tumor core, and enhancing tumor, respectively.作者: 驚奇 時(shí)間: 2025-4-1 15:36
A Theoretical Framework of Mixed Systemsace in the final ranking on unseen test data, achieving a dice score of 88.35%, 88.78%, 93.19% for the enhancing tumor, the tumor core, and the whole tumor, respectively. The codes, pretrained weights, and docker image for the winning submission are publicly available. (. .)作者: covert 時(shí)間: 2025-4-1 20:44 作者: Thymus 時(shí)間: 2025-4-2 01:00
