標(biāo)題: Titlebook: ; [打印本頁] 作者: risky-drinking 時(shí)間: 2025-3-21 19:07
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities影響因子(影響力)
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities影響因子(影響力)學(xué)科排名
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities網(wǎng)絡(luò)公開度
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities被引頻次
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities被引頻次學(xué)科排名
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities年度引用
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities年度引用學(xué)科排名
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities讀者反饋
書目名稱Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities讀者反饋學(xué)科排名
作者: 橫條 時(shí)間: 2025-3-21 23:01 作者: 豎琴 時(shí)間: 2025-3-22 01:33 作者: 十字架 時(shí)間: 2025-3-22 08:26 作者: 橫截,橫斷 時(shí)間: 2025-3-22 09:36 作者: 血統(tǒng) 時(shí)間: 2025-3-22 16:43 作者: 血統(tǒng) 時(shí)間: 2025-3-22 19:32 作者: Allergic 時(shí)間: 2025-3-22 21:23
https://doi.org/10.1007/978-4-431-53924-7o called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. W作者: 肉體 時(shí)間: 2025-3-23 01:58
https://doi.org/10.1007/978-1-4613-0777-8. Even for 2017, there were published more than hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI conver作者: 凝視 時(shí)間: 2025-3-23 05:35
https://doi.org/10.1007/978-3-642-75952-9ons/compulsions, and limited insight. They also show partially overlapping patterns of brain activation, white matter connectivity, and electrophysiological responses. These markers have also shown associations with symptom severity within each disorder. We aimed to determine: (a) if, cross-diagnost作者: SOW 時(shí)間: 2025-3-23 10:03 作者: 鞭打 時(shí)間: 2025-3-23 16:18 作者: 樹膠 時(shí)間: 2025-3-23 20:53
Multi-modal Disease Classification in Incomplete Datasets Using Geometric Matrix Completionof multi-modal data, including imaging and other sensor data, clinical scores, phenotypes, labels and demographics. However, missing features, rater bias and inaccurate measurements are typical ailments of real-life medical datasets. Recently, it has been shown that deep learning with graph convolut作者: encyclopedia 時(shí)間: 2025-3-24 02:00
BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classificationevel brain graph into a number of subgraphs, which are assumed to represent “homogeneous” brain regions with respect to a predefined criteria. Aforementioned brain graph is constructed by a set of local meshes, called mesh networks. Then, the supervoxels are obtained using a graph partitioning algor作者: Cpr951 時(shí)間: 2025-3-24 03:51 作者: Wallow 時(shí)間: 2025-3-24 08:25
A Bayesian Disease Progression Model for Clinical Trajectoriesrt with mild symptoms that might precede a diagnosis, and each patient follows their own trajectory. Patient trajectories exhibit wild variability, which can be associated with many factors such as genotype, age, or sex. An additional layer of complexity is that, in real life, the amount and type of作者: 外向者 時(shí)間: 2025-3-24 14:31
Multi-modal Brain Connectivity Study Using Deep Collaborative Learningrelation analysis (CCA) based models, have been used to detect correlations and to analyze brain connectivities which further help explore how the brain works. However, the data representation of CCA lacks label related information and may be limited when applied to functional connectivity study. Co作者: champaign 時(shí)間: 2025-3-24 16:49
Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functiono called “disconnection hypothesis” suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. W作者: 沖擊力 時(shí)間: 2025-3-24 20:17
Predicting Conversion of Mild Cognitive Impairments to Alzheimer’s Disease and?Exploring Impact of N. Even for 2017, there were published more than hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI conver作者: Maximizer 時(shí)間: 2025-3-25 01:09 作者: 治愈 時(shí)間: 2025-3-25 03:52 作者: 玩笑 時(shí)間: 2025-3-25 08:14
Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functionk (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.作者: Audiometry 時(shí)間: 2025-3-25 15:01
Mycotoxins in Plants and Plant Productstially close voxels. This study shows that BrainParcel can achieve higher accuracies in cognitive state classification compared to AAL. It has a better representation power compared to similar brain segmentation methods, reported the literature.作者: 強(qiáng)壯 時(shí)間: 2025-3-25 18:11
BrainParcel: A Brain Parcellation Algorithm for Cognitive State Classificationtially close voxels. This study shows that BrainParcel can achieve higher accuracies in cognitive state classification compared to AAL. It has a better representation power compared to similar brain segmentation methods, reported the literature.作者: flavonoids 時(shí)間: 2025-3-25 22:53
Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities作者: 性行為放縱者 時(shí)間: 2025-3-26 00:40 作者: Expiration 時(shí)間: 2025-3-26 07:33 作者: 整潔漂亮 時(shí)間: 2025-3-26 09:02 作者: Forsake 時(shí)間: 2025-3-26 16:14 作者: textile 時(shí)間: 2025-3-26 20:23
A Bayesian Disease Progression Model for Clinical Trajectoriescores at future time-points. We use a sigmoidal function to model latent disease progression, which gives rise to clinical observations in our generative model. We implemented an approximate Bayesian inference strategy on the proposed model to estimate the parameters on data from a large population 作者: 幻想 時(shí)間: 2025-3-26 21:19
Multi-modal Brain Connectivity Study Using Deep Collaborative Learningning correlation analysis and label information using deep networks, which may lead to better performance both for classification/prediction and for correlation detection. Results demonstrated the out-performance of DCL over other conventional models in terms of classification accuracy. Experiments 作者: 火光在搖曳 時(shí)間: 2025-3-27 04:06 作者: Evocative 時(shí)間: 2025-3-27 07:31
Cross-diagnostic Prediction of Dimensional Psychiatric Phenotypes in Anorexia Nervosa and Body Dysmodict dimensional phenotypes of insight and obsession/compulsions across a sample of unmedicated adults with BDD (n?=?29) and weight-restored AN (n?=?24). The multivariate model that included fMRI and white matter connectivity data performed significantly better in predicting both insight and obsessi作者: lesion 時(shí)間: 2025-3-27 09:58 作者: Bravura 時(shí)間: 2025-3-27 15:12 作者: seduce 時(shí)間: 2025-3-27 18:59 作者: Hdl348 時(shí)間: 2025-3-27 22:04
https://doi.org/10.1007/978-1-4757-9450-2eatures compared to the undirected ones for recognizing the cognitive processes. The representation power of the suggested brain networks are tested in a task-fMRI dataset of Human Connectome Project and a Complex Problem Solving dataset.作者: Expertise 時(shí)間: 2025-3-28 05:21
https://doi.org/10.1007/978-1-4615-7514-6cores at future time-points. We use a sigmoidal function to model latent disease progression, which gives rise to clinical observations in our generative model. We implemented an approximate Bayesian inference strategy on the proposed model to estimate the parameters on data from a large population 作者: Fecal-Impaction 時(shí)間: 2025-3-28 06:26 作者: 無能力之人 時(shí)間: 2025-3-28 11:58 作者: 熱情的我 時(shí)間: 2025-3-28 16:12 作者: modifier 時(shí)間: 2025-3-28 19:36 作者: 向外才掩飾 時(shí)間: 2025-3-29 02:37
Einleitung,lich zu machen und Kontakt miteinander aufzunehmen. In Gesten drücken sich soziale Beziehungen und Emotionen aus, die oft weder denen bewusst sind, die sie vollziehen, noch ins Bewusstsein derer gelangen, die sie wahrnehmen und auf sie reagieren. Gesten begleiten die gesprochene Sprache und haben zu作者: BARK 時(shí)間: 2025-3-29 05:43 作者: Canary 時(shí)間: 2025-3-29 10:23
Frequency Histograms and Checksheets,dererseits von der Gestalt des K?rpers und von der Art der Belastung, die er aufzunehmen hat. Vor allem ist es wichtig, die Abh?ngigkeit von dem Stoffe festzustellen. Das kann nur auf dem Versuchswege geschehen. Zu diesem Zwecke stellt man aus dem betreffenden Stoffe einen K?rper von m?glichst einfa
