標題: Titlebook: ; [打印本頁] 作者: Clique 時間: 2025-3-21 17:55
書目名稱Graph Learning in Medical Imaging影響因子(影響力)
書目名稱Graph Learning in Medical Imaging影響因子(影響力)學(xué)科排名
書目名稱Graph Learning in Medical Imaging網(wǎng)絡(luò)公開度
書目名稱Graph Learning in Medical Imaging網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Graph Learning in Medical Imaging被引頻次
書目名稱Graph Learning in Medical Imaging被引頻次學(xué)科排名
書目名稱Graph Learning in Medical Imaging年度引用
書目名稱Graph Learning in Medical Imaging年度引用學(xué)科排名
書目名稱Graph Learning in Medical Imaging讀者反饋
書目名稱Graph Learning in Medical Imaging讀者反饋學(xué)科排名
作者: 表示問 時間: 2025-3-21 20:31
https://doi.org/10.1007/978-3-322-85959-4and ultimately avoiding the use of atlases and any registration method. We evaluate DeepBundle using data from the Human Connectome Project. Experimental results demonstrate the advantages of DeepBundle and suggest that the geometric features extracted from each fiber tract can be used to effectively parcellate the fiber tracts.作者: 相容 時間: 2025-3-22 02:39
https://doi.org/10.1007/978-3-322-80069-5tion demonstrates that our model is implicitly consistent with the pixel-wise segmentation labels, which indicates our model can identify the region of interests without relying on the pixel-wise labels.作者: Impugn 時間: 2025-3-22 08:33
https://doi.org/10.1007/978-3-662-12498-7ew loss function that learns the geometrical relationships between the landmarks in the form of a root/leaf structure. We evaluate our approach on 49 CBCT scans of patients and achieve an average detection error of 1.75?±?0.91?mm. Experimental results show that our approach overperforms the related methods in the term of accuracy.作者: 易于交談 時間: 2025-3-22 11:51
Versicherung und Risikoforschung10 was achieved for DDSM and 0.893 for BSSA. The results indicate that graph models can capture texture features capable of identifying masses located in dense tissues, and help improve computer-aided detection systems.作者: 實施生效 時間: 2025-3-22 16:33 作者: 實施生效 時間: 2025-3-22 18:23 作者: 放逐 時間: 2025-3-22 22:53
Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images,tion demonstrates that our model is implicitly consistent with the pixel-wise segmentation labels, which indicates our model can identify the region of interests without relying on the pixel-wise labels.作者: 撫育 時間: 2025-3-23 04:56 作者: 中子 時間: 2025-3-23 07:19 作者: arthrodesis 時間: 2025-3-23 11:47
https://doi.org/10.1007/978-3-662-08744-2 to eliminate the variability of anatomical structure and functional topography of the human brain, which calls for aligning fMRI data across subjects. However, the existing methods do not exploit the geometry of the stimuli, which can be inferred by using certain domain knowledge and then serve as 作者: 變形 時間: 2025-3-23 16:23 作者: 褻瀆 時間: 2025-3-23 20:57
https://doi.org/10.1007/978-3-658-02813-8of brain diseases, such as attention deficit hyperactivity disorder (ADHD) and Alzheimer’s disease (AD). To generate compact representations of FC networks for disease analysis, various thresholding strategies have been developed for analyzing brain FC networks. However, existing studies typically e作者: Cuisine 時間: 2025-3-24 02:16
https://doi.org/10.1007/978-3-658-30067-8ases, such as Alzheimer’s disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (., local clustering coefficients) from FCNs as a feature vector for brain disease classification, and perform vector-based feature selection meth作者: 熱心助人 時間: 2025-3-24 06:07
https://doi.org/10.1007/978-3-322-87806-9n learn features from Euclidean data such as images. In this work, we propose a novel method to combine CNNs with GCNs (CNN-GCN), that can consider both Euclidean and non-Euclidean features and can be trained end-to-end. We applied this method to separate the pulmonary vascular trees into arteries a作者: frozen-shoulder 時間: 2025-3-24 09:32
https://doi.org/10.1007/978-3-7091-7620-7 this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-作者: Carbon-Monoxide 時間: 2025-3-24 14:04
https://doi.org/10.1007/b138008 from the recent advances in medical deep learning, the same does not yet apply for the registration of point sets, e.g. registration based on surfaces, keypoints or landmarks. This is mainly due to the restriction of the convolution operator in modern CNNs to densely gridded input. However, with th作者: 倔強不能 時間: 2025-3-24 17:23
https://doi.org/10.1007/978-3-7091-8940-5his work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen. Predictions for individual vertex locations are based on local image features as well as on features of neighboring vertices 作者: coagulation 時間: 2025-3-24 22:47
https://doi.org/10.1007/978-3-7091-9073-9tivity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constru作者: buoyant 時間: 2025-3-24 23:11 作者: conduct 時間: 2025-3-25 03:54
https://doi.org/10.1007/978-3-322-85959-4r parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, however, due to large individual differences, accurate registration is hard to guarantee in practice. To resolve this issue, we propose a novel deep learning method, called DeepBundle, for 作者: 健談 時間: 2025-3-25 07:41
https://doi.org/10.1007/978-3-7091-9214-6 information can be used to improve diagnosis. The heterogeneity of depression suggests that diverse circuit-level abnormalities in individuals lead to various symptoms. Investigating heterogeneous depression is crucial to understand disease mechanisms and provide personalised medicine. Dynamical fu作者: 放肆的我 時間: 2025-3-25 14:43 作者: 非秘密 時間: 2025-3-25 19:01
https://doi.org/10.1007/978-3-322-80069-5grating patch level information into the whole image level prediction. Also, it is often difficult to obtain sufficient high-quality patch labels such as pixel-wise segmentation masks. Benefiting from the recent development of Graph-CNN (GCN), we propose a new weakly- and semi-supervised GCN archite作者: 方舟 時間: 2025-3-25 20:56 作者: 種類 時間: 2025-3-26 01:12 作者: 俗艷 時間: 2025-3-26 07:56 作者: amygdala 時間: 2025-3-26 08:42
Versicherung und Risikoforschungdy, a novel superpixel based graph modeling technique is proposed to extract texture features from the computer identified suspicious regions of mammograms. Graph models are constructed from specific structured superpixel patterns and used to generate feature vectors used for classifications of regi作者: 油膏 時間: 2025-3-26 14:06
https://doi.org/10.1007/978-3-658-02654-7move redundant information in resting-state functional magnetic resonance imaging (rs-fMRI) data via the brain functional connectivity network (BFCN) and retain good biological characteristics, it is an important method for OCD analysis. However, most existing methods ignore the relationship among s作者: 帳單 時間: 2025-3-26 19:54 作者: 混沌 時間: 2025-3-27 00:09 作者: 取消 時間: 2025-3-27 01:46 作者: Dna262 時間: 2025-3-27 06:54
Graph-Kernel-Based Multi-task Structured Feature Selection on Multi-level Functional Connectivity Nases, such as Alzheimer’s disease (AD) and Attention Deficit Hyperactivity Disorder (ADHD). However, existing studies usually extract meaningful measures (., local clustering coefficients) from FCNs as a feature vector for brain disease classification, and perform vector-based feature selection meth作者: 密切關(guān)系 時間: 2025-3-27 10:15 作者: GRAIN 時間: 2025-3-27 14:51
Comparative Analysis of Magnetic Resonance Fingerprinting Dictionaries via Dimensionality Reduction this work we present a method in which we approach analysis of MRF dictionaries by performing dimensionality reduction and representing them as low-dimensional point sets (embeddings). Dimensionality reduction was performed using a modification of the t-Distributed Stochastic Neighbor Embedding (t-作者: OASIS 時間: 2025-3-27 21:15
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motio from the recent advances in medical deep learning, the same does not yet apply for the registration of point sets, e.g. registration based on surfaces, keypoints or landmarks. This is mainly due to the restriction of the convolution operator in modern CNNs to densely gridded input. However, with th作者: Infraction 時間: 2025-3-28 00:26 作者: 堅毅 時間: 2025-3-28 05:22
Triplet Graph Convolutional Network for Multi-scale Analysis of Functional Connectivity Using Functtivity. Using fMRI data, graph convolutional network (GCN) has recently shown its superiority in learning discriminative representations of brain FC networks. However, existing studies typically utilize one specific template to partition the brain into multiple regions-of-interest (ROIs) for constru作者: 脫落 時間: 2025-3-28 07:28
Multi-scale Graph Convolutional Network for Mild Cognitive Impairment Detection,ly consider neuroimaging features learned from group relationships instead of the subjects’ individual features. Such methods ignore demographic relationships (i.e., non-image information). In this paper, we propose a novel method based on multi-scale graph convolutional network (MS-GCN) via incepti作者: 松緊帶 時間: 2025-3-28 11:03
DeepBundle: Fiber Bundle Parcellation with Graph Convolution Neural Networks,r parcellation approaches rely on accurate registration between an atlas and the tractograms of an individual, however, due to large individual differences, accurate registration is hard to guarantee in practice. To resolve this issue, we propose a novel deep learning method, called DeepBundle, for 作者: brachial-plexus 時間: 2025-3-28 15:33 作者: outskirts 時間: 2025-3-28 22:31 作者: 泰然自若 時間: 2025-3-29 01:50
Weakly- and Semi-supervised Graph CNN for Identifying Basal Cell Carcinoma on Pathological Images,grating patch level information into the whole image level prediction. Also, it is often difficult to obtain sufficient high-quality patch labels such as pixel-wise segmentation masks. Benefiting from the recent development of Graph-CNN (GCN), we propose a new weakly- and semi-supervised GCN archite作者: 腫塊 時間: 2025-3-29 06:14 作者: 不利 時間: 2025-3-29 09:56
Automatic Detection of Craniomaxillofacial Anatomical Landmarks on CBCT Images Using 3D Mask R-CNN,the complexity of facial structure and the deformities of CMF patients, it is still difficult to accurately localize a large scale of landmarks simultaneously. In this work, we propose a three-stage coarse-to-fine deep learning method for digitizing 105 anatomical craniomaxillofacial landmarks on co作者: 結(jié)果 時間: 2025-3-29 14:04 作者: PAC 時間: 2025-3-29 19:38 作者: Spirometry 時間: 2025-3-29 21:49
OCD Diagnosis via Smoothing Sparse Network and Stacked Sparse Auto-Encoder Learning,move redundant information in resting-state functional magnetic resonance imaging (rs-fMRI) data via the brain functional connectivity network (BFCN) and retain good biological characteristics, it is an important method for OCD analysis. However, most existing methods ignore the relationship among s作者: 氣候 時間: 2025-3-30 01:19 作者: AORTA 時間: 2025-3-30 06:42 作者: 迅速成長 時間: 2025-3-30 08:25 作者: 出汗 時間: 2025-3-30 13:01
https://doi.org/10.1007/978-3-658-30067-8 the structural information of FCNs, and uses the multi-task learning to explore the complementary information of multi-level thresholded FCNs (., thresholded FCNs with different thresholds). Specifically, in the proposed gk-MTSFS model, we first develop a novel graph-kernel based Laplacian regulari作者: Tinea-Capitis 時間: 2025-3-30 17:47 作者: 背景 時間: 2025-3-30 22:55 作者: Reservation 時間: 2025-3-31 04:38
https://doi.org/10.1007/978-3-7091-8940-5 0.25/0.28?mm, and a Hausdorff distance of 1.53/1.86?mm in healthy/diseased vessel segments. The results showed that inclusion of mesh information in a GCN improves segmentation overlap and accuracy over a baseline model without interaction on the mesh. The results indicate that GCNs allow efficient作者: perpetual 時間: 2025-3-31 06:51
https://doi.org/10.1007/978-3-7091-9073-9ation to construct multi-scale FCs for each subject. We then develop a triplet GCN (TGCN) model to learn multi-scale graph representations of brain FC networks, followed by a weighted fusion scheme for classification. Experimental results on 1,218 subjects suggest the efficacy or our method.作者: Mettle 時間: 2025-3-31 09:22 作者: grounded 時間: 2025-3-31 15:12 作者: Fibroid 時間: 2025-3-31 17:55 作者: 幸福愉悅感 時間: 2025-4-1 00:13 作者: Cpap155 時間: 2025-4-1 02:52 作者: PLAYS 時間: 2025-4-1 09:42
https://doi.org/10.1007/978-3-658-02654-7res of BFCN and reduce the data dimension. We add a .-norm to prevent overfitting as well. We apply this framework on OCD dataset self-collected from local hospitals. The experimental results show that our method can achieve quite promising performance and outperform the state-of-the-art methods.
