標(biāo)題: Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020; 23rd International C Anne L. Martel,Purang Abolmaesumi,Leo Joskow [打印本頁(yè)] 作者: FROM 時(shí)間: 2025-3-21 19:14
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020影響因子(影響力)學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開(kāi)度
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020被引頻次學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020年度引用學(xué)科排名
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋
書目名稱Medical Image Computing and Computer Assisted Intervention – MICCAI 2020讀者反饋學(xué)科排名
作者: Ingenuity 時(shí)間: 2025-3-21 22:36
978-3-030-59727-6Springer Nature Switzerland AG 2020作者: Entirety 時(shí)間: 2025-3-22 01:28 作者: 平息 時(shí)間: 2025-3-22 07:02
https://doi.org/10.1007/978-3-030-59728-3artificial intelligence; computer aided diagnosis; computer vision; image analysis; image processing; ima作者: abnegate 時(shí)間: 2025-3-22 09:00 作者: 妨礙 時(shí)間: 2025-3-22 13:07 作者: 一個(gè)姐姐 時(shí)間: 2025-3-22 18:16
Parkinson’s Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networks after employing an SVM classifier, achieve a sensitivity of disease detection of 94% – comparable to approaches that normally require whole-brain analysis. To the best of our knowledge, this is the first study that employs brainstem functional sub-regions for Parkinson’s disease detection.作者: Pantry 時(shí)間: 2025-3-23 01:05 作者: AFFIX 時(shí)間: 2025-3-23 04:43 作者: CHIDE 時(shí)間: 2025-3-23 07:50 作者: 手榴彈 時(shí)間: 2025-3-23 13:00
Domain-Invariant Prior Knowledge Guided Attention Networks for Robust Skull Stripping of Developing edge, which are important guidance information for accurate brain extraction of developing macaques from 0 to 36 months of age. Specifically, we introduce signed distance map (SDM) and center of gravity distance map (CGDM) based on the intermediate segmentation results and fuse their information by 作者: amorphous 時(shí)間: 2025-3-23 15:07 作者: Progesterone 時(shí)間: 2025-3-23 21:04
Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generative-layer graph convolution networks (GCNs) which have the capability to model complex indirect connections in brain connectivity. The discriminator of MGCN-GAN is a single multi-layer GCN which aims to distinguish predicted SC from real SC. To overcome the inherent unstable behavior of GAN, we designe作者: facilitate 時(shí)間: 2025-3-24 00:52
Disentangled Intensive Triplet Autoencoder for Infant Functional Connectome Fingerprintingiminative capability among infant individuals. Then, a disentanglement strategy is proposed to separate the latent variables into identity-code, age-code, and noise-code, which not only restrains the interference from age-related developmental variance, but also captures the identity-related invaria作者: 飾帶 時(shí)間: 2025-3-24 04:56 作者: Carcinogenesis 時(shí)間: 2025-3-24 09:11
Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regressionession method is developed in the attempt to automatically identified the species-shared and -specific connections. The concordance of the findings . our method and previous reports demonstrate the effectiveness and the promise of this framework.作者: PRE 時(shí)間: 2025-3-24 14:19 作者: Minatory 時(shí)間: 2025-3-24 17:06
Unified Brain Network with Functional and Structural Dataifold with structural data into this model. The constructed network then captures the global brain region correlation by the low-rank constraint and preserves the local structural information by manifold learning. Second, we adaptively estimate the importance of different brain regions by PageRank a作者: 服從 時(shí)間: 2025-3-24 19:17
Integrating Similarity Awareness and Adaptive Calibration in Graph Convolution Network to Predict Diural scores. Current edge weights are used to construct an initial graph and .-. the GCN. Based on the pre-trained GCN, the differences between scores replace the traditional correlation distances to evaluate edge weights. Lastly, we devise a . technique to . functional and structural information fo作者: 牛的細(xì)微差別 時(shí)間: 2025-3-25 00:33
Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Featurering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed meth作者: Ptsd429 時(shí)間: 2025-3-25 06:44 作者: 雄辯 時(shí)間: 2025-3-25 07:51
Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templateading to error accumulation. To address these issues, we propose Deep Graph Normalizer (DGN), . for normalizing a population of MVBNs by integrating them into a single connectional brain template. Our end-to-end DGN learns how to fuse multi-view brain networks while capturing non-linear patterns acr作者: 使激動(dòng) 時(shí)間: 2025-3-25 14:53
Supervised Multi-topology Network Cross-Diffusion for Population-Driven Brain Network Atlas Estimatii) well-centeredness (optimally close to all subjects), and (iii) high discriminativeness (can easily and efficiently identify discriminative brain connections that distinguish between two populations). For a specific class, given the cluster labels of the training data, we . a weighted combination 作者: 不整齊 時(shí)間: 2025-3-25 19:53 作者: dowagers-hump 時(shí)間: 2025-3-25 22:04
Conference proceedings 2020analysis..Part IV: segmentation; shape models and landmark detection..Part V: biological, optical, microscopic imaging; cell segmentation and stain normalization; histopathology image analysis; opthalmology..Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal im作者: 宿醉 時(shí)間: 2025-3-26 02:22
0302-9743 d stain normalization; histopathology image analysis; opthalmology..Part VI: angiography and vessel analysis; breast imaging; colonoscopy; dermatology; fetal im978-3-030-59727-6978-3-030-59728-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: Peristalsis 時(shí)間: 2025-3-26 08:23 作者: white-matter 時(shí)間: 2025-3-26 11:24
Mustafa Burak Gurbuz,Islem Rekikdes..Luisa Peters ist wissenschaftliche Mitarbeiterin am Institut für Sozial- und Organisationsp?dagogik an der Universit?t Hildesheim..Dr. Andreas Herz ist w978-3-658-20371-9978-3-658-20372-6Series ISSN 2512-1170 Series E-ISSN 2512-1189 作者: fringe 時(shí)間: 2025-3-26 16:24 作者: 種屬關(guān)系 時(shí)間: 2025-3-26 19:25
Maryam Ghanbari,Li-Ming Hsu,Zhen Zhou,Amir Ghanbari,Zhanhao Mo,Pew-Thian Yap,Han Zhang,Dinggang Shen作者: 擴(kuò)音器 時(shí)間: 2025-3-26 23:17 作者: Hirsutism 時(shí)間: 2025-3-27 01:55 作者: 感激小女 時(shí)間: 2025-3-27 06:20
Nandinee Fariah Haq,Jiayue Cai,Tianze Yu,Martin J. McKeown,Z. Jane Wang作者: 談判 時(shí)間: 2025-3-27 11:31
Jin Li,Chenyuan Bian,Dandan Chen,Xianglian Meng,Haoran Luo,Hong Liang,Li Shen作者: Oscillate 時(shí)間: 2025-3-27 16:02 作者: 討好女人 時(shí)間: 2025-3-27 18:22 作者: 人類的發(fā)源 時(shí)間: 2025-3-28 00:24 作者: 搬運(yùn)工 時(shí)間: 2025-3-28 05:11 作者: Antecedent 時(shí)間: 2025-3-28 08:30 作者: 改變立場(chǎng) 時(shí)間: 2025-3-28 11:13
Medical Image Computing and Computer Assisted Intervention – MICCAI 202023rd International C作者: 不如樂(lè)死去 時(shí)間: 2025-3-28 17:10 作者: 古文字學(xué) 時(shí)間: 2025-3-28 20:34 作者: DOTE 時(shí)間: 2025-3-29 00:51
Benjamin Billot,Eleanor Robinson,Adrian V. Dalca,Juan Eugenio Iglesiasst wissenschaftlicher Mitarbeiter (Post-Doc) an der Fakult?t für Sozialwissenschaften an der Hochschule für Technik und Wirtschaft des Saarlandes..Luisa Peters ist wissenschaftliche Mitarbeiterin am Institut für Sozial- und Organisationsp?dagogik an der Universit?t Hildesheim..Dr. Andreas Herz ist w作者: 輕信 時(shí)間: 2025-3-29 05:00
Xuegang Song,Alejandro Frangi,Xiaohua Xiao,Jiuwen Cao,Tianfu Wang,Baiying Leisen in Organisationen.Beschreibung des Wandels von Organisat.Kaum ein Begriff wird im Managementdiskurs so leichtfertig gebraucht wie der der Organisationskultur. W?hrend es in der Diskussion bisher eher üblich ist, den Begriff weit zu definieren und darunter so unterschiedliche Ph?nomene wie ?Annah作者: 離開(kāi)真充足 時(shí)間: 2025-3-29 10:03
Xin Zhang,Jiale Cheng,Hao Ni,Chenyang Li,Xiangmin Xu,Zhengwang Wu,Li Wang,Weili Lin,Dinggang Shen,Gasen in Organisationen.Beschreibung des Wandels von Organisat.Kaum ein Begriff wird im Managementdiskurs so leichtfertig gebraucht wie der der Organisationskultur. W?hrend es in der Diskussion bisher eher üblich ist, den Begriff weit zu definieren und darunter so unterschiedliche Ph?nomene wie ?Annah作者: 我沒(méi)有強(qiáng)迫 時(shí)間: 2025-3-29 14:35 作者: 圍裙 時(shí)間: 2025-3-29 18:34 作者: Lipoma 時(shí)間: 2025-3-29 23:31 作者: 沉著 時(shí)間: 2025-3-30 03:20
Benjamin Billot,Eleanor Robinson,Adrian V. Dalca,Juan Eugenio Iglesiasht die Verbindung zwischen sozialer Netzwerkforschung und erziehungswissenschaftlicher/ organisationsp?dagogischer Diskussion.?.?.Der Inhalt.Theorie und Methodologie in der Netzwerk- und Organisationsforschung ? Organisationales Lernen und Steuerung in und von Netzwerken ? Vernetzung, soziale Dienst作者: 走調(diào) 時(shí)間: 2025-3-30 04:25
A New Metric for Characterizing Dynamic Redundancy of Dense Brain Chronnectome and Its Application t propose a new metric to measure how the brain network is robust or resilient to any attack on its nodes and edges. The metric measures redundancy in the sense that it calculates the minimum number of independent, not necessarily shortest, paths between every pair of nodes. We adopt this metric for 作者: Hot-Flash 時(shí)間: 2025-3-30 11:58 作者: facilitate 時(shí)間: 2025-3-30 15:46 作者: 串通 時(shí)間: 2025-3-30 20:02
Parkinson’s Disease Detection from fMRI-Derived Brainstem Regional Functional Connectivity Networksvolvement in Parkinson’s disease, is largely unexplored in the domain of functional medical imaging. Here we propose a data-driven, connectivity-pattern based framework to extract functional sub-regions within the brainstem and devise a machine learning based tool that can discriminate Parkinson’s d作者: 話 時(shí)間: 2025-3-30 22:11
Persistent Feature Analysis of Multimodal Brain Networks Using Generalized Fused Lasso for EMCI Iden By jointly analyzing cross-information among different neuroimaging data, an increased interest recently emerges in multimodal fusion to better understand clinical measurements with respect to both structural and functional connectivity. In this paper, we propose a novel multimodal brain network mo作者: 投射 時(shí)間: 2025-3-31 01:52
Recovering Brain Structural Connectivity from Functional Connectivity via Multi-GCN Based Generativeis critical for revealing organizational principles of human brain. However, brain’s many-to-one function-structure mode, i.e., diverse functional patterns may be associated with the same SC, and the complex direct/indirect interactions in both structural and functional connectivity make it challeng作者: Excise 時(shí)間: 2025-3-31 06:11 作者: fatuity 時(shí)間: 2025-3-31 13:03 作者: obsession 時(shí)間: 2025-3-31 15:11 作者: 惹人反感 時(shí)間: 2025-3-31 20:31
Species-Shared and -Specific Structural Connections Revealed by Dirty Multi-task Regressione common ancestors and the specific ones that might be related to individualized evolution strategies. Both the shared or specific patterns could help promote the understanding of mechanisms of brain structural and functional architectures and brain dynamics. Many previous studies can be found to re作者: dysphagia 時(shí)間: 2025-4-1 01:12
Self-weighted Multi-task Learning for Subjective Cognitive Decline Diagnosis’s disease (AD). Early diagnosis of MCI is important because early identification and intervention can delay or even reverse the progression of this disease. This paper proposes an automatic diagnostic framework for SCD and MCI. Specifically, we design a new multi-task learning model to integrate ne作者: synovial-joint 時(shí)間: 2025-4-1 04:51
Unified Brain Network with Functional and Structural Databeen made to merge functional network and structural network. Whether using single modal or multi-modal data, the construction of brain network plays an important role in the whole diagnosis system. However, the existing multi-modal brain network analysis methods usually construct functional network作者: 偽造 時(shí)間: 2025-4-1 08:10 作者: 人類學(xué)家 時(shí)間: 2025-4-1 14:05
