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: Covenant 時(shí)間: 2025-3-21 18:52
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: 簡(jiǎn)略 時(shí)間: 2025-3-21 22:13 作者: 牢騷 時(shí)間: 2025-3-22 03:03
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries978-3-031-08999-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 系列 時(shí)間: 2025-3-22 04:58 作者: miniature 時(shí)間: 2025-3-22 11:06
David M. Mosser,C. Andrew Stewartn/reconstruction to segmentation/classification to outcome prediction. Specifically, these models can help improve the efficiency and accuracy of image interpretation and quantification. However, it is important to note the challenges of working with medical imaging data, and how this can affect the作者: 束縛 時(shí)間: 2025-3-22 13:13
Kupffer Cells in Health and Diseases the input feature maps into three parts with ., . and . convolutions in both encoder and decoder. Concat operator is used to merge the features before being fed to three consecutive transformer blocks with attention mechanism embedded inside it. Skip connections are used to connect encoder and dec作者: INCH 時(shí)間: 2025-3-22 21:02
A. Elger,M. H. Barrat-Segretain,N. J. Willbyin clinical practice are usually determined based on multi-modal data, especially for tumor diseases. In this paper, we intend to find a way to effectively fuse radiology images and pathology images for the diagnosis of gliomas. To this end, we propose a collaborative attention network (CA-Net), whi作者: dendrites 時(shí)間: 2025-3-22 23:34
Ute Feiler,Falk Krebs,Peter Heiningerf automatically identifying pathologies in brain images. Our work challenges the effectiveness of current Machine Learning (ML) approaches in this application domain by showing that thresholding Fluid-attenuated inversion recovery (FLAIR) MR scans provides better anomaly segmentation maps than sever作者: 競(jìng)選運(yùn)動(dòng) 時(shí)間: 2025-3-23 02:26 作者: 事先無(wú)準(zhǔn)備 時(shí)間: 2025-3-23 06:50 作者: Morose 時(shí)間: 2025-3-23 12:35
Macropinocytosis and Cell Migration: s work, we present a framework for the evaluation of growth predictions that focuses on the spatial infiltration patterns, and specifically evaluating a prediction of future growth. We propose to frame the problem as a ranking problem rather than a segmentation problem. Using the average precision a作者: Notify 時(shí)間: 2025-3-23 14:58 作者: Genetics 時(shí)間: 2025-3-23 21:10
Guillem Lambies,Cosimo Commissoad to more precise treatment. With unsupervised learning techniques, glioblastoma MRI-derived radiomic features have been widely utilized for tumor sub-region segmentation and survival prediction. However, the reliability of algorithm outcomes is often challenged by both ambiguous intermediate proce作者: 非秘密 時(shí)間: 2025-3-23 22:23 作者: spinal-stenosis 時(shí)間: 2025-3-24 02:54
A Legal Approach to Monetary Policy,pathologically affected brains, and hence tend to suffer in performance when applied on brains with pathologies, e.g., gliomas, multiple sclerosis, traumatic brain injuries. Deep Learning (DL) methodologies for healthcare have shown promising results, but their clinical translation has been limited,作者: regale 時(shí)間: 2025-3-24 08:37 作者: 類型 時(shí)間: 2025-3-24 13:43 作者: evanescent 時(shí)間: 2025-3-24 15:20 作者: 水獺 時(shí)間: 2025-3-24 21:33
Abdul Ghafar Ismail,Zuriyati Ahmadmages using deep learning methods is critical for gliomas diagnosis. Deep learning segmentation architectures, especially based on fully convolutional neural network, have proved great performance on medical image segmentation. However, these approaches cannot explicitly model global information and作者: MONY 時(shí)間: 2025-3-25 02:10 作者: Fierce 時(shí)間: 2025-3-25 03:31
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries7th International Wo作者: 神圣在玷污 時(shí)間: 2025-3-25 09:46 作者: Binge-Drinking 時(shí)間: 2025-3-25 15:10
A. Elger,M. H. Barrat-Segretain,N. J. Willbyentary information from the other modality. The cross attention matrixes imply the feature reliability, so they are further utilized to obtain a coefficient for each modality to linearly fuse the enhanced features as the final representation in the attention fusion module. The three attention module作者: Ondines-curse 時(shí)間: 2025-3-25 18:19
J. M. Caffrey,A. Dutartre,P. M. WadeTo obtain the output at the original scale, we propose a learnable downsampler (as opposed to hand-crafted ones e.g. bilinear) that combines subpixel predictions. Our approach improves the baseline architecture by .11.7% and achieves the state of the art on the ATLAS public benchmark dataset with a 作者: 巨頭 時(shí)間: 2025-3-25 21:21 作者: 無(wú)情 時(shí)間: 2025-3-26 02:20 作者: 無(wú)思維能力 時(shí)間: 2025-3-26 08:12 作者: 脆弱吧 時(shí)間: 2025-3-26 12:20
A Legal Approach to Monetary Policy,work template of healthy subjects, consisting of atlases of edges (white matter tracts) and nodes (cortical/subcortical brain regions) to provide regions of interest (ROIs). Next, we employ autoencoders to extract the latent multi-modal MRI features from the ROIs of edges and nodes in patients, to t作者: GAVEL 時(shí)間: 2025-3-26 14:30
A Legal Approach to Monetary Policy,ffected brains and agnostic to the input modality. We conduct direct optimizations and quantization of the trained model (i.e., prior to inference on new data). Our results yield substantial gains, in terms of speedup, latency, throughput, and reduction in memory usage, while the segmentation perfor作者: cumber 時(shí)間: 2025-3-26 16:50
The Conduct of Macroprudential Policy for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our approach achieved a Dice Similarity Score of 84.55%, 90.46% and 85.30%, as well as Hausdorff Distance (95%) of 13.48?mm, 6.32?mm and 16.98?mm on the final test dataset. Overall, our proposed approach yielded better pe作者: 內(nèi)閣 時(shí)間: 2025-3-26 21:24
Abdul Ghafar Ismail,Zuriyati Ahmadmentation performance. COTRNet achieved dice scores of ., ., and . in the enhancing tumor, the tumor core, and the whole tumor segmentation on brain tumor segmentation challenge 2021. Experimental results demonstrated effectiveness of the proposed method.作者: MAUVE 時(shí)間: 2025-3-27 05:08 作者: institute 時(shí)間: 2025-3-27 05:52
CA-Net: Collaborative Attention Network for?Multi-modal Diagnosis of?Gliomasentary information from the other modality. The cross attention matrixes imply the feature reliability, so they are further utilized to obtain a coefficient for each modality to linearly fuse the enhanced features as the final representation in the attention fusion module. The three attention module作者: 商談 時(shí)間: 2025-3-27 12:35
Small Lesion Segmentation in?Brain MRIs with?Subpixel EmbeddingTo obtain the output at the original scale, we propose a learnable downsampler (as opposed to hand-crafted ones e.g. bilinear) that combines subpixel predictions. Our approach improves the baseline architecture by .11.7% and achieves the state of the art on the ATLAS public benchmark dataset with a 作者: Esalate 時(shí)間: 2025-3-27 17:06
Unsupervised Multimodal Supervoxel Merging Towards Brain Tumor Segmentationategy produces high-quality clustering results useful for brain tumor segmentation. Indeed, our method reaches an ASA score of 0.712 compared to 0.316 for the monomodal approach, indicating that the supervoxels accommodate well tumor boundaries. Our approach also improves by 11.5% the Global Score (作者: conifer 時(shí)間: 2025-3-27 20:12
Modeling Multi-annotator Uncertainty as?Multi-class Segmentation ProblemQ (Quantification of Uncertainties in Biomedical Image Quantification) challenges. We achieved high quality segmentation results, despite a small set of training samples, and at time of this writing achieved an overall third and sixth best result on the respective QUBIQ 2020 and 2021 challenge leade作者: Critical 時(shí)間: 2025-3-27 23:20 作者: Bridle 時(shí)間: 2025-3-28 05:39
Predicting Isocitrate Dehydrogenase Mutation Status in?Glioma Using Structural Brain Networks and?Grwork template of healthy subjects, consisting of atlases of edges (white matter tracts) and nodes (cortical/subcortical brain regions) to provide regions of interest (ROIs). Next, we employ autoencoders to extract the latent multi-modal MRI features from the ROIs of edges and nodes in patients, to t作者: admission 時(shí)間: 2025-3-28 06:23 作者: Favorable 時(shí)間: 2025-3-28 12:48
Reciprocal Adversarial Learning for?Brain Tumor Segmentation: A Solution to?BraTS Challenge 2021 Seg for the enhancing tumor, whole tumor and tumor core, respectively. Similarly, our approach achieved a Dice Similarity Score of 84.55%, 90.46% and 85.30%, as well as Hausdorff Distance (95%) of 13.48?mm, 6.32?mm and 16.98?mm on the final test dataset. Overall, our proposed approach yielded better pe作者: 呼吸 時(shí)間: 2025-3-28 14:51 作者: Promotion 時(shí)間: 2025-3-28 21:06
Conference proceedings 2022es 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These w作者: 發(fā)炎 時(shí)間: 2025-3-28 23:44 作者: Cosmopolitan 時(shí)間: 2025-3-29 03:04 作者: 單片眼鏡 時(shí)間: 2025-3-29 08:21
Opportunities and?Challenges for?Deep Learning in?Brain Lesionsre deep learning has been used to improve upon the current standard of care for brain lesions. We conclude with a section on some of the current challenges and hurdles facing neuroimaging researchers.作者: eustachian-tube 時(shí)間: 2025-3-29 14:53
Evaluating Glioma Growth Predictions as?a?Forward Ranking Problemr growth model to 21 patient cases we compare two schemes for fitting and evaluating predictions. By carefully designing a scheme that separates the prediction from the observations used for fitting the model, we show that a better fit of model parameters does not guarantee a better predictive power.作者: 結(jié)構(gòu) 時(shí)間: 2025-3-29 16:41 作者: Generator 時(shí)間: 2025-3-29 20:47 作者: NUDGE 時(shí)間: 2025-3-30 03:31 作者: GRILL 時(shí)間: 2025-3-30 07:43
0302-9743 evised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually..978-3-031-08998-5978-3-031-08999-2Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 革新 時(shí)間: 2025-3-30 10:47 作者: creditor 時(shí)間: 2025-3-30 13:42
https://doi.org/10.1057/9781137274465sing patches and batch 2 to save GPU memory usage. Online validation of the segmentation results from the BraTS 2021 validation dataset resulted in dice performance of 78.02, 80.73, and 89.07 for ET, TC, and WT. These results indicate that the proposed architecture is promising for further development.作者: 娘娘腔 時(shí)間: 2025-3-30 18:50 作者: Fabric 時(shí)間: 2025-3-30 22:20 作者: lipoatrophy 時(shí)間: 2025-3-31 03:13
Unet3D with?Multiple Atrous Convolutions Attention Block for?Brain Tumor Segmentationsing patches and batch 2 to save GPU memory usage. Online validation of the segmentation results from the BraTS 2021 validation dataset resulted in dice performance of 78.02, 80.73, and 89.07 for ET, TC, and WT. These results indicate that the proposed architecture is promising for further development.作者: membrane 時(shí)間: 2025-3-31 05:51 作者: acclimate 時(shí)間: 2025-3-31 10:27
Ute Feiler,Falk Krebs,Peter Heiningeral different ML-based anomaly detection models. Specifically, our method achieves better Dice similarity coefficients and Precision-Recall curves than the competitors on various popular evaluation data sets for the segmentation of tumors and multiple sclerosis lesions. (Code available under: .)作者: AMBI 時(shí)間: 2025-3-31 13:29
A Review of?Medical Federated Learning: Applications in?Oncology and?Cancer Researchmpact in healthcare, with numerous applications and intelligent systems achieving clinical level expertise. However, building robust and generalizable systems relies on training algorithms in a centralized fashion using large, heterogeneous datasets. In medicine, these datasets are time consuming to作者: Obituary 時(shí)間: 2025-3-31 18:20
Opportunities and?Challenges for?Deep Learning in?Brain Lesionsn/reconstruction to segmentation/classification to outcome prediction. Specifically, these models can help improve the efficiency and accuracy of image interpretation and quantification. However, it is important to note the challenges of working with medical imaging data, and how this can affect the作者: corn732 時(shí)間: 2025-3-31 22:48
EMSViT: Efficient Multi Scale Vision Transformer for?Biomedical Image Segmentations the input feature maps into three parts with ., . and . convolutions in both encoder and decoder. Concat operator is used to merge the features before being fed to three consecutive transformer blocks with attention mechanism embedded inside it. Skip connections are used to connect encoder and dec作者: 新義 時(shí)間: 2025-4-1 02:12
CA-Net: Collaborative Attention Network for?Multi-modal Diagnosis of?Gliomasin clinical practice are usually determined based on multi-modal data, especially for tumor diseases. In this paper, we intend to find a way to effectively fuse radiology images and pathology images for the diagnosis of gliomas. To this end, we propose a collaborative attention network (CA-Net), whi
