2015 International Workshop on Pattern Recognition in NeuroImaging最新文献_第2页

Speeding-Up Model-Selection in Graphnet via Early-Stopping and Univariate Feature-Screening 基于早期停止和单变量特征筛选的Graphnet加速模型选择

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.19

Elvis Dohmatob, Michael Eickenberg, B. Thirion, G. Varoquaux

引用次数: 10

Predicting Skill-Based Task Performance and Learning with fMRI Motor and Subcortical Network Connectivity 功能磁共振成像运动和皮层下网络连通性预测基于技能的任务表现和学习

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.35

A. Nikolaidis, Drew Goatz, P. Smaragdis, A. Kramer

引用次数: 6

Predicting Numerical Processing in Naturalistic Settings from Controlled Experimental Conditions 预测数值处理在自然设置从控制实验条件

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.13

J. Schrouff, C. Phillips, J. Parvizi, J. Miranda

引用次数: 1

Multivariate Effect Ranking via Adaptive Sparse PLS 基于自适应稀疏PLS的多变量效应排序

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.27

J. Monteiro, A. Rao, J. Ashburner, J. Shawe-Taylor, J. Miranda

{"title":"Multivariate Effect Ranking via Adaptive Sparse PLS","authors":"J. Monteiro, A. Rao, J. Ashburner, J. Shawe-Taylor, J. Miranda","doi":"10.1109/PRNI.2015.27","DOIUrl":"https://doi.org/10.1109/PRNI.2015.27","url":null,"abstract":"Unsupervised learning approaches, such as Sparse Partial Least Squares (SPLS), may provide useful insights into the brain mechanisms by finding relationships between two sets of variables (i.e. Views) from the same subjects. The algorithm outputs two sets of paired weight vectors, where each pair expresses an \"effect\" between both views. However, each effect can be described by a different number of variables. In this paper, we propose a novel approach to find multivariate associations between combinations of clinical/behavioural variables and brain voxels/regions which provides an unique solution with different levels of sparsity per weight vector pair. The effects described by the weight vector pairs are ranked by how much data covariance they explain. The proposed method was able to find statistically significant effects or relationships in a dementia dataset between clinical/demographic information and brain scans. Its adaptive nature allowed not only to determine an optimal sparse solution, but also provided the flexibility to select the adequate number of clinical/demographic variables and voxels to describe each effect, which enabled it to distinguish the effects associated with age from the ones associated with dementia.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122686819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Improving Sparse Recovery on Structured Images with Bagged Clustering 利用袋装聚类改进结构化图像的稀疏恢复

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.30

Andrés Hoyos Idrobo, Y. Schwartz, G. Varoquaux, B. Thirion

引用次数: 20

Variational Physiologically Informed Solution to Hemodynamic and Perfusion Response Estimation from ASL fMRI Data 从ASL功能磁共振成像数据估计血流动力学和灌注反应的变化生理学解决方案

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.12

Aina Frau-Pascual, F. Forbes, P. Ciuciu

引用次数: 2

Automatic Brain Tumor Segmentation from MR Images via a Multimodal Sparse Coding Based Probabilistic Model 基于多模态稀疏编码概率模型的MR图像脑肿瘤自动分割

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.18

Yuhong Li, Q. Dou, Jinze Yu, F. Jia, Jing Qin, P. Heng

{"title":"Automatic Brain Tumor Segmentation from MR Images via a Multimodal Sparse Coding Based Probabilistic Model","authors":"Yuhong Li, Q. Dou, Jinze Yu, F. Jia, Jing Qin, P. Heng","doi":"10.1109/PRNI.2015.18","DOIUrl":"https://doi.org/10.1109/PRNI.2015.18","url":null,"abstract":"Accurate segmentation of brain tumor from MR image is crucial for the diagnosis and treatment of brain cancer. We propose a novel automated brain tumor segmentation method based on a probabilistic model combining sparse coding and Markov random field (MRF). We formulate the brain tumor segmentation task as a pixel-wise labeling problem with regard to three classes: tumor, edema and healthy issue. For each class, dictionary learning is performed independently on multi-modality gray scale patches. Sparse representation is then extracted based on a joint dictionary which is constructed by combing the three independent dictionaries. Finally, we build the probabilistic model aiming to estimate maximum a posterior (MAP) probability by introducing the sparse representation into likelihood probability and prior probability using the Markov random field (MRF) assumption. Compared with traditional methods, which employed hand-crafted low level features to construct the probabilistic model, our model can better represent the characteristics of a pixel and its relation with neighbors based on the sparse coefficients obtained from the learned dictionary. We validated our method on the MICAAI 2012 BRATS challenge brain MRI dataset and achieved comparable or better results compared with state-of the-art methods.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132612358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Modeling Voxel Connectivity for Brain Decoding 脑解码体素连接建模

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.26

Itir Önal, M. Ozay, F. Yarman-Vural

{"title":"Modeling Voxel Connectivity for Brain Decoding","authors":"Itir Önal, M. Ozay, F. Yarman-Vural","doi":"10.1109/PRNI.2015.26","DOIUrl":"https://doi.org/10.1109/PRNI.2015.26","url":null,"abstract":"The massively dynamic nature of human brain cannot be represented by considering only a collection of voxel intensity values obtained from fMRI measurements. It has been observed that the degree of connectivity among voxels provide important information for modeling cognitive activities. Moreover, spatially close voxels act together to generate similar BOLD responses to the same stimuli. In this study, we propose a local mesh model, called Local Mesh Model with Temporal Measurements (LMM-TM), to first estimate spatial relationship among a set of voxels using spatial and temporal data measured at each voxel, and then employ the relationship for the construction of a connectivity model for brain decoding. For this purpose, we first construct a local mesh around each voxel (called seed voxel) by connecting it to its spatially nearest neighbors. Then, we represent the BOLD response of each seed voxel in terms of linear combination of the BOLD responses of its p-nearest neighbors. The relationship between a seed voxel and its neighbors is estimated by solving a linear regression problem. The estimated mesh arc weights are used to model local connectivity among the voxels that reside in a spatial neighborhood. Using these weights as features, we train Support Vector Machines and k-Nearest Neighbor classifiers. We test our model on a visual object recognition experiment. In the experimental analysis, we observe that classifiers that employ our features perform better than classifiers that employ raw voxel intensity values, local mesh model weights and features extracted using distance metrics such as Euclidean distance, cosine similarity and Pearson correlation.","PeriodicalId":380902,"journal":{"name":"2015 International Workshop on Pattern Recognition in NeuroImaging","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131141967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

A Bayesian Test for Comparing Classifier Errors 比较分类器误差的贝叶斯检验

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.11

E. Olivetti, Dirk Bernhardt-Walther

引用次数: 2

Bolstering Heuristics for Statistical Validation of Prediction Algorithms 预测算法统计验证的增强启发式

2015 International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2015-06-10 DOI: 10.1109/PRNI.2015.16

Alex F. Mendelson, Maria A. Zuluaga, B. Hutton, S. Ourselin

引用次数: 2