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] 作者: 表范圍 時間: 2025-3-21 18:43
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
書目名稱Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries被引頻次學(xué)科排名
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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: 租約 時間: 2025-3-21 21:24 作者: 思考才皺眉 時間: 2025-3-22 03:05 作者: Custodian 時間: 2025-3-22 05:04 作者: ESPY 時間: 2025-3-22 10:22 作者: 騙子 時間: 2025-3-22 14:51 作者: BUDGE 時間: 2025-3-22 20:26 作者: Rinne-Test 時間: 2025-3-23 00:26 作者: 厚顏 時間: 2025-3-23 01:49
Fiscal Policies in Germany, France and Italyhapes and positions. Our network architecture is based on the Multipath Convolutional Neural Network?[.], which considers both local and contextual patches of segmentation information, including original MRI images, symmetry information and spatial information. Motivated to reduce the feature loss i作者: Irrepressible 時間: 2025-3-23 09:32 作者: GRAIN 時間: 2025-3-23 09:54 作者: Tracheotomy 時間: 2025-3-23 17:44 作者: 手段 時間: 2025-3-23 20:28 作者: 誓言 時間: 2025-3-24 00:49
Monetary and Fiscal Cooperationple sclerosis lesions from multimodal magnetic resonance images. The proposed model is made as a combination of two deep subnetworks. An encoding network extracts different feature maps at various resolutions. A decoding part upconvolves the feature maps combining them through shortcut connections d作者: 直覺好 時間: 2025-3-24 05:05
Monetary and Fiscal Competitions of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we ev作者: MURAL 時間: 2025-3-24 07:33 作者: Extort 時間: 2025-3-24 12:48 作者: uncertain 時間: 2025-3-24 16:21 作者: 金哥占卜者 時間: 2025-3-24 19:46
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/190319.jpg作者: 審問,審訊 時間: 2025-3-24 23:35
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries978-3-030-11723-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: FRAX-tool 時間: 2025-3-25 05:38
Multimodal Patho-Connectomics of Brain Injurynalysis of patho-connectomes are discussed in the case of brain tumors, that suffers from the challenges of mass effect and infiltration of the peritumoral region, which in turn affect the surgical and radiation plan, and in traumatic brain injury, where the exact injury may be difficult to determin作者: 記憶法 時間: 2025-3-25 09:49
Adverse Effects of Image Tiling on Convolutional Neural Networksnt of tile overlap, but this comes at a greater computational cost and still produces inferior results to using the whole image..Although tiling has been used to produce acceptable segmentation results in the past, we recommend performing inference on the whole image to achieve the best results and 作者: 漸變 時間: 2025-3-25 12:13
MIMoSA: An Approach to Automatically Segment T2 Hyperintense and T1 Hypointense Lesions in Multiple S?rensen-Dice coefficient (DSC) of 0.6 and partial AUC (pAUC) up to 1% false positive rate of 0.69 were achieved. For T1L, 0.48 DSC and 0.63 pAUC were achieved. The correlation between EDSS and manual versus automatic volumes were similar for T1L (0.32 manual vs. 0.34 MIMoSA) and T2L (0.34 vs. 0.34作者: ablate 時間: 2025-3-25 17:01 作者: PLIC 時間: 2025-3-25 22:14 作者: 衣服 時間: 2025-3-26 01:44 作者: Lignans 時間: 2025-3-26 05:40 作者: inscribe 時間: 2025-3-26 10:05 作者: FECK 時間: 2025-3-26 16:02
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..978-3-030-11722-1978-3-030-11723-8Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 鼓掌 時間: 2025-3-26 18:13
Macroeconomics from the Bottom-upnalysis of patho-connectomes are discussed in the case of brain tumors, that suffers from the challenges of mass effect and infiltration of the peritumoral region, which in turn affect the surgical and radiation plan, and in traumatic brain injury, where the exact injury may be difficult to determin作者: convert 時間: 2025-3-26 23:02
https://doi.org/10.1007/978-3-319-51757-5nt of tile overlap, but this comes at a greater computational cost and still produces inferior results to using the whole image..Although tiling has been used to produce acceptable segmentation results in the past, we recommend performing inference on the whole image to achieve the best results and 作者: humectant 時間: 2025-3-27 03:50 作者: Adulterate 時間: 2025-3-27 06:26 作者: Exploit 時間: 2025-3-27 13:15 作者: glowing 時間: 2025-3-27 14:51
https://doi.org/10.1007/978-3-540-73633-2 to patient specific fine-tuning. The proposed method is computationally fast and efficient as compared to state-of-the-art interactive segmentation tools. This tool could be useful for post-surgical treatment follow-up with minimal user intervention.作者: tangle 時間: 2025-3-27 21:30 作者: Altitude 時間: 2025-3-27 22:50
Monetary and Fiscal Competition and even delineate anomalies in brain MR images by simply comparing input images to their reconstruction. Results show that constraints on the latent space and adversarial training can further improve the segmentation performance over standard deep representation learning.作者: 背信 時間: 2025-3-28 05:51 作者: Defraud 時間: 2025-3-28 06:33
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries4th International Wo作者: 放牧 時間: 2025-3-28 13:07
0302-9743 Les 2018, as well as the International Multimodal Brain Tumor Segmentation, BraTS, Ischemic Stroke Lesion Segmentation, ISLES,? MR Brain Image Segmentation, MRBrainS18, Computational Precision Medicine, CPM, and ?Stroke Workshop on Imaging and Treatment Challenges, SWITCH, which were held jointly at作者: painkillers 時間: 2025-3-28 15:19
Macroeconomics from the Bottom-upthe clinical applications of CTP, its advantages over MRI and disadvantages. Factors affecting the results of CTP will also be discussed. Finally, a clinically oriented overview of the calculated perfusion parameters and their value will be provided.作者: doxazosin 時間: 2025-3-28 21:06
CT Brain Perfusion: A Clinical Perspectivethe clinical applications of CTP, its advantages over MRI and disadvantages. Factors affecting the results of CTP will also be discussed. Finally, a clinically oriented overview of the calculated perfusion parameters and their value will be provided.作者: 羅盤 時間: 2025-3-29 00:21 作者: coltish 時間: 2025-3-29 04:23 作者: 猛烈責(zé)罵 時間: 2025-3-29 07:27 作者: crucial 時間: 2025-3-29 15:13
Conference proceedings 2019me were carefully reviewed and selected from 95 submissions. They were organized in topical sections named: brain lesion image analysis; brain tumor image segmentation; ischemic stroke lesion image segmentation; grand challenge on MR brain segmentation; computational precision medicine; stroke workshop on imaging and treatment challenges..作者: 性上癮 時間: 2025-3-29 18:37 作者: ESPY 時間: 2025-3-29 22:59 作者: BAIT 時間: 2025-3-30 02:30 作者: 來就得意 時間: 2025-3-30 07:21
Monetary and Fiscal Competitionshallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).作者: 贊美者 時間: 2025-3-30 11:57 作者: JIBE 時間: 2025-3-30 15:53
Simultaneous Decisions: Cold-Turkey Policies models, it obtains a testing accuracy of . without any additional effort towards extraction and selection of features. We also study the properties of self-learned kernels/filters in different layers, through visualization of the intermediate layer outputs.作者: incisive 時間: 2025-3-30 20:02 作者: Patrimony 時間: 2025-3-30 21:23
Multipath Densely Connected Convolutional Neural Network for Brain Tumor Segmentationomplete segmentation network. The model’s training and validation are performed on the BraTS2017 dataset. Experimental results demonstrate that the proposed network is capable to effectively extract more accurate tumor locations and contours with improved stability.作者: 公司 時間: 2025-3-31 00:52
Multi-institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Braiodal brain scans is similar to that of models trained by sharing data (Dice?=?0.862). We compare federated learning with two alternative collaborative learning methods and find that they fail to match the performance of federated learning.作者: 碌碌之人 時間: 2025-3-31 06:27
Shallow vs Deep Learning Architectures for White Matter Lesion Segmentation in the Early Stages of Mshallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).
