Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops最新文献_第6页

A Database Developed with Information Extracted from Chemotherapy Drug Package Inserts to Enhance Future Prescriptions. 从化疗药物说明书中提取信息的数据库开发，以提高未来处方。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2011-01-01

Malcolm J D'Souza, Ghada J Alabed, Jordan M Wheatley, Natalia Roberts, Yogasudha Veturi, Xia Bi, Christopher Hart Continisio

{"title":"A Database Developed with Information Extracted from Chemotherapy Drug Package Inserts to Enhance Future Prescriptions.","authors":"Malcolm J D'Souza, Ghada J Alabed, Jordan M Wheatley, Natalia Roberts, Yogasudha Veturi, Xia Bi, Christopher Hart Continisio","doi":"","DOIUrl":"","url":null,"abstract":"Package inserts of Food and Drug Administration (FDA) approved prescription drugs, including chemotherapy drugs, must follow a specific format imposed by the FDA. These inserts are created by unrelated pharmaceutical companies and as a result tend to be very different in the way the required information is reported. Chemical and pharmacokinetic properties including absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) are crucial elements to a prescribing information packet and are often missing from the reported data. This undergraduate research project analyzes the information packets of 85 randomly chosen chemically diverse chemotherapy drugs for four parameters important to patient care; viz, volume of distribution (VD), elimination half-life (t1/2), bioavailability, and water solubility. The prescribing information from the package inserts of each was analyzed in detail and pertinent information was consequently tabulated into a database using a commercial informatics platform. Then using a substructure search-tool, sixty-five chemotherapy drugs containing a carbonyl group in their chemical structure were selected and as hypothesized, it was found that many of these packets were significantly lacking in the reporting of the four parameters of interest. To further enhance this cataloged data, a freely available online database was consequently developed (http://annotation.dbi.udel.edu/CancerDB/) with the intention that the chemical, biological, and clinical community will now add some of the missing parameters.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"2011 ","pages":"219-226"},"PeriodicalIF":0.0,"publicationDate":"2011-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4187114/pdf/nihms312037.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32735489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vascular Tree Reconstruction by Minimizing A Physiological Functional Cost. 最小化生理功能成本重建血管树。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-06-13 DOI: 10.1109/CVPRW.2010.5543593

Yifeng Jiang, Zhenwu Zhuang, Albert J Sinusas, Xenophon Papademetris

引用次数: 0

Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation. 脑功能分割的非参数层次贝叶斯模型。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-06-13 DOI: 10.1109/CVPRW.2010.5543434

Danial Lashkari, Ramesh Sridharan, Edward Vul, Po-Jang Hsieh, Nancy Kanwisher, Polina Golland

{"title":"Nonparametric Hierarchical Bayesian Model for Functional Brain Parcellation.","authors":"Danial Lashkari, Ramesh Sridharan, Edward Vul, Po-Jang Hsieh, Nancy Kanwisher, Polina Golland","doi":"10.1109/CVPRW.2010.5543434","DOIUrl":"10.1109/CVPRW.2010.5543434","url":null,"abstract":"We develop a method for unsupervised analysis of functional brain images that learns group-level patterns of functional response. Our algorithm is based on a generative model that comprises two main layers. At the lower level, we express the functional brain response to each stimulus as a binary activation variable. At the next level, we define a prior over the sets of activation variables in all subjects. We use a Hierarchical Dirichlet Process as the prior in order to simultaneously learn the patterns of response that are shared across the group, and to estimate the number of these patterns supported by data. Inference based on this model enables automatic discovery and characterization of salient and consistent patterns in functional signals. We apply our method to data from a study that explores the response of the visual cortex to a collection of images. The discovered profiles of activation correspond to selectivity to a number of image categories such as faces, bodies, and scenes. More generally, our results appear superior to the results of alternative data-driven methods in capturing the category structure in the space of stimuli.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":" ","pages":"15-22"},"PeriodicalIF":0.0,"publicationDate":"2010-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3153957/pdf/nihms-272409.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"30078482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Topology-Preserving STAPLE. Topology-Preserving主食。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-06-01 DOI: 10.1109/CVPRW.2010.5543195

John A Bogovic, Pierre-Louis Bazin, Jerry L Prince

引用次数: 0

Groupwise Morphometric Analysis Based on High Dimensional Clustering. 基于高维聚类的群体形态计量分析。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-06-01 Epub Date: 2010-08-09 DOI: 10.1109/CVPRW.2010.5543438

Dong Hye Ye, Kilian M Pohl, Harold Litt, Christos Davatzikos

{"title":"Groupwise Morphometric Analysis Based on High Dimensional Clustering.","authors":"Dong Hye Ye, Kilian M Pohl, Harold Litt, Christos Davatzikos","doi":"10.1109/CVPRW.2010.5543438","DOIUrl":"10.1109/CVPRW.2010.5543438","url":null,"abstract":"In this paper, we propose an efficient groupwise morphometric analysis to characterize morphological variations between healthy and pathological states. The proposed framework extends the work of Baloch [4] in which a manifold for each anatomy was constructed by collecting lossless [transformation, residual] descriptors with various transformation parameters, and the optimal set of transformation parameters was estimated individually by minimizing group variance. However, full parameter exploration is not desired as it can result in transformation leading to inaccurate anatomical models. In addition, a single fixed template introduces a priori bias to subsequent statistical analysis. In order to overcome these limitations, we use an affinity propagation clustering method to find the spatially close cluster center for each subject. Then, a subject is normalized to the template via the cluster center to restrict our analysis only to those descriptors that reflect reasonable warps. In addition, a mean template is selected by finding a cluster center that minimizes the sum of pairwise shape distance to reduce the fixed template bias. Our method is applied to 2D synthetic data and 3D real Cardiac MR Images. Experimental results show improvement in quantifying and localizing shape changes.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"2010 ","pages":"47-54"},"PeriodicalIF":0.0,"publicationDate":"2010-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPRW.2010.5543438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35080488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Two-Point Correlation as a Feature for Histology Images: Feature Space Structure and Correlation Updating. 两点相关作为组织学图像的特征:特征空间结构与相关更新。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-01-01 DOI: 10.1109/CVPRW.2010.5543453

Lee Cooper, Joel Saltz, Raghu Machiraju, Kun Huang

{"title":"Two-Point Correlation as a Feature for Histology Images: Feature Space Structure and Correlation Updating.","authors":"Lee Cooper, Joel Saltz, Raghu Machiraju, Kun Huang","doi":"10.1109/CVPRW.2010.5543453","DOIUrl":"https://doi.org/10.1109/CVPRW.2010.5543453","url":null,"abstract":"The segmentation of tissues in whole-slide histology images is a necessary step for the morphological analyses of tissues and cellular structures. Previous works have demonstrated the potential of two-point correlation functions (TPCF) as features for tissue segmentation, however the feature space is not yet well understood and computational methods are lacking. This paper illustrates several fundamental aspects of TPCF feature space and contributes a fast algorithm for deterministic feature computation. Despite the high-dimensionality of TPCF feature space, the features corresponding to different tissues are shown to be characterized by low-dimensional manifolds. The relationship between TPCF and the familiar co-occurrence matrix is highlighted, and it is shown that costly cross correlations are not necessary to achieve an accurate segmentation. For computation, the method of correlation updating, based on the linearity of the correlation operator, is proposed and shown to achieve up to a 67X speedup over frequency domain computation methods. Segmentation results are demonstrated on multiple tissues and natural texture images.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":" ","pages":"79-86"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPRW.2010.5543453","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40261302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Anatomical Parts-Based Regression Using Non-Negative Matrix Factorization. 利用非负矩阵因式分解实现基于解剖部位的回归。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-01-01 DOI: 10.1109/CVPR.2010.5540022

Swapna Joshi, S Karthikeyan, B S Manjunath, Scott Grafton, Kent A Kiehl

{"title":"Anatomical Parts-Based Regression Using Non-Negative Matrix Factorization.","authors":"Swapna Joshi, S Karthikeyan, B S Manjunath, Scott Grafton, Kent A Kiehl","doi":"10.1109/CVPR.2010.5540022","DOIUrl":"10.1109/CVPR.2010.5540022","url":null,"abstract":"Non-negative matrix factorization (NMF) is an excellent tool for unsupervised parts-based learning, but proves to be ineffective when parts of a whole follow a specific pattern. Analyzing such local changes is particularly important when studying anatomical transformations. We propose a supervised method that incorporates a regression constraint into the NMF framework and learns maximally changing parts in the basis images, called Regression based NMF (RNMF). The algorithm is made robust against outliers by learning the distribution of the input manifold space, where the data resides. One of our main goals is to achieve good region localization. By incorporating a gradient smoothing and independence constraint into the factorized bases, contiguous local regions are captured. We apply our technique to a synthetic dataset and structural MRI brain images of subjects with varying ages. RNMF finds the localized regions which are expected to be highly changing over age to be manifested in its significant basis and it also achieves the best performance compared to other statistical regression and dimensionality reduction techniques.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":" ","pages":"2863-2870"},"PeriodicalIF":0.0,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4059066/pdf/nihms580671.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32437653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust Feature Selection in Resting-State fMRI Connectivity Based on Population Studies. 基于种群研究的静息状态fMRI连接鲁棒特征选择。

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2010-01-01 DOI: 10.1109/CVPRW.2010.5543446

Archana Venkataraman, Marek Kubicki, Carl-Fredrik Westin, Polina Golland

引用次数: 0

Recognizing realistic actions from videos 从视频中识别真实的动作

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206744

Jingen Liu, Jiebo Luo, M. Shah

{"title":"Recognizing realistic actions from videos","authors":"Jingen Liu, Jiebo Luo, M. Shah","doi":"10.1109/CVPR.2009.5206744","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206744","url":null,"abstract":"In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild”. Such unconstrained videos are abundant in personal collections as well as on the Web. Recognizing action from such videos has not been addressed extensively, primarily due to the tremendous variations that result from camera motion, background clutter, changes in object appearance, and scale, etc. The main challenge is how to extract reliable and informative features from the unconstrained videos. We extract both motion and static features from the videos. Since the raw features of both types are dense yet noisy, we propose strategies to prune these features. We use motion statistics to acquire stable motion features and clean static features. Furthermore, PageRank is used to mine the most informative static features. In order to further construct compact yet discriminative visual vocabularies, a divisive information-theoretic algorithm is employed to group semantically related features. Finally, AdaBoost is chosen to integrate all the heterogeneous yet complementary features for recognition. We have tested the framework on the KTH dataset and our own dataset consisting of 11 categories of actions collected from YouTube and personal videos, and have obtained impressive results for action recognition and action localization.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"49 1","pages":"1996-2003"},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83205990","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: 255

Multiplicative nonnegative graph embedding 乘法非负图嵌入

Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops Pub Date : 2009-06-20 DOI: 10.1109/CVPR.2009.5206865

Changhu Wang, Zheng Song, Shuicheng Yan, Lei Zhang, HongJiang Zhang

{"title":"Multiplicative nonnegative graph embedding","authors":"Changhu Wang, Zheng Song, Shuicheng Yan, Lei Zhang, HongJiang Zhang","doi":"10.1109/CVPR.2009.5206865","DOIUrl":"https://doi.org/10.1109/CVPR.2009.5206865","url":null,"abstract":"In this paper, we study the problem of nonnegative graph embedding, originally investigated in [J. Yang et al., 2008] for reaping the benefits from both nonnegative data factorization and the specific purpose characterized by the intrinsic and penalty graphs. Our contributions are two-fold. On the one hand, we present a multiplicative iterative procedure for nonnegative graph embedding, which significantly reduces the computational cost compared with the iterative procedure in [14] involving the matrix inverse calculation of an M-matrix. On the other hand, the nonnegative graph embedding framework is expressed in a more general way by encoding each datum as a tensor of arbitrary order, which brings a group of byproducts, e.g., nonnegative discriminative tensor factorization algorithm, with admissible time and memory cost. Extensive experiments compared with the state-of-the-art algorithms on nonnegative data factorization, graph embedding, and tensor representation demonstrate the algorithmic properties in computation speed, sparsity, discriminating power, and robustness to realistic image occlusions.","PeriodicalId":89346,"journal":{"name":"Conference on Computer Vision and Pattern Recognition Workshops. IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Workshops","volume":"98 1","pages":"389-396"},"PeriodicalIF":0.0,"publicationDate":"2009-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79422715","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: 26