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

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-01 DOI: 10.1109/CVPR.2014.395

Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen

{"title":"Matrix-Similarity Based Loss Function and Feature Selection for Alzheimer's Disease Diagnosis.","authors":"Xiaofeng Zhu, Heung-Il Suk, Dinggang Shen","doi":"10.1109/CVPR.2014.395","DOIUrl":"https://doi.org/10.1109/CVPR.2014.395","url":null,"abstract":"Recent studies on Alzheimer's Disease (AD) or its prodromal stage, Mild Cognitive Impairment (MCI), diagnosis presented that the tasks of identifying brain disease status and predicting clinical scores based on neuroimaging features were highly related to each other. However, these tasks were often conducted independently in the previous studies. Regarding the feature selection, to our best knowledge, most of the previous work considered a loss function defined as an element-wise difference between the target values and the predicted ones. In this paper, we consider the problems of joint regression and classification for AD/MCI diagnosis and propose a novel matrix-similarity based loss function that uses high-level information inherent in the target response matrix and imposes the information to be preserved in the predicted response matrix. The newly devised loss function is combined with a group lasso method for joint feature selection across tasks, i.e., clinical scores prediction and disease status identification. We conducted experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and showed that the newly devised loss function was effective to enhance the performances of both clinical score prediction and disease status identification, outperforming the state-of-the-art methods.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2014 ","pages":"3089-3096"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2014.395","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34079156","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: 71

Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields. 基于张量场的特征赋值点集可变形配准。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-01 DOI: 10.1109/CVPR.2014.355

Demian Wassermann, James Ross, George Washko, William M Wells, Raul San Jose-Estepar

{"title":"Deformable Registration of Feature-Endowed Point Sets Based on Tensor Fields.","authors":"Demian Wassermann, James Ross, George Washko, William M Wells, Raul San Jose-Estepar","doi":"10.1109/CVPR.2014.355","DOIUrl":"https://doi.org/10.1109/CVPR.2014.355","url":null,"abstract":"The main contribution of this work is a framework to register anatomical structures characterized as a point set where each point has an associated symmetric matrix. These matrices can represent problem-dependent characteristics of the registered structure. For example, in airways, matrices can represent the orientation and thickness of the structure. Our framework relies on a dense tensor field representation which we implement sparsely as a kernel mixture of tensor fields. We equip the space of tensor fields with a norm that serves as a similarity measure. To calculate the optimal transformation between two structures we minimize this measure using an analytical gradient for the similarity measure and the deformation field, which we restrict to be a diffeomorphism. We illustrate the value of our tensor field model by comparing our results with scalar and vector field based models. Finally, we evaluate our registration algorithm on synthetic data sets and validate our approach on manually annotated airway trees.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2014 ","pages":"2729-2735"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2014.355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32878931","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: 4

A Riemannian framework for matching point clouds represented by the Schrödinger distance transform. 以薛定谔距离变换为代表的点云匹配黎曼框架。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2014-06-01 DOI: 10.1109/CVPR.2014.486

Yan Deng, Anand Rangarajan, Stephan Eisenschenk, Baba C Vemuri

{"title":"A Riemannian framework for matching point clouds represented by the Schrödinger distance transform.","authors":"Yan Deng, Anand Rangarajan, Stephan Eisenschenk, Baba C Vemuri","doi":"10.1109/CVPR.2014.486","DOIUrl":"10.1109/CVPR.2014.486","url":null,"abstract":"In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrödinger distance transform (SDT) representation. This is achieved by solving a static Schrödinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit L2 norm-making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm. We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques. Finally, to evaluate the performance of our algorithm-dubbed SDTM-we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques. The experiments show that our algorithm outperforms state-of-the-art point set registration algorithms on many quantitative metrics.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2014 ","pages":"3756-3761"},"PeriodicalIF":0.0,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4374547/pdf/nihms670513.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33171178","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

Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis. AD诊断的联合耦合特征表示与耦合增强。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2014-01-01 DOI: 10.1109/CVPR.2014.354

Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen

{"title":"Joint Coupled-Feature Representation and Coupled Boosting for AD Diagnosis.","authors":"Yinghuan Shi, Heung-Il Suk, Yang Gao, Dinggang Shen","doi":"10.1109/CVPR.2014.354","DOIUrl":"https://doi.org/10.1109/CVPR.2014.354","url":null,"abstract":"Recently, there has been a great interest in computer-aided Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) diagnosis. Previous learning based methods defined the diagnosis process as a classification task and directly used the low-level features extracted from neuroimaging data without considering relations among them. However, from a neuroscience point of view, it's well known that a human brain is a complex system that multiple brain regions are anatomically connected and functionally interact with each other. Therefore, it is natural to hypothesize that the low-level features extracted from neuroimaging data are related to each other in some ways. To this end, in this paper, we first devise a coupled feature representation by utilizing intra-coupled and inter-coupled interaction relationship. Regarding multi-modal data fusion, we propose a novel coupled boosting algorithm that analyzes the pairwise coupled-diversity correlation between modalities. Specifically, we formulate a new weight updating function, which considers both incorrectly and inconsistently classified samples. In our experiments on the ADNI dataset, the proposed method presented the best performance with accuracies of 94.7% and 80.1% for AD vs. Normal Control (NC) and MCI vs. NC classifications, respectively, outperforming the competing methods and the state-of-the-art methods.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2014 ","pages":"2721-2728"},"PeriodicalIF":0.0,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2014.354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34278709","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: 28

Classification of Tumor Histology via Morphometric Context. 通过形态学内涵对肿瘤组织学进行分类。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-23 DOI: 10.1109/CVPR.2013.286

Hang Chang, Alexander Borowsky, Paul Spellman, Bahram Parvin

{"title":"Classification of Tumor Histology via Morphometric Context.","authors":"Hang Chang, Alexander Borowsky, Paul Spellman, Bahram Parvin","doi":"10.1109/CVPR.2013.286","DOIUrl":"10.1109/CVPR.2013.286","url":null,"abstract":"Image-based classification of tissue histology, in terms of different components (e.g., normal signature, categories of aberrant signatures), provides a series of indices for tumor composition. Subsequently, aggregation of these indices in each whole slide image (WSI) from a large cohort can provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical and biological variations that are always present in a large cohort. In this paper, we propose two algorithms for classification of tissue histology based on robust representations of morphometric context, which are built upon nuclear level morphometric features at various locations and scales within the spatial pyramid matching (SPM) framework. These methods have been evaluated on two distinct datasets of different tumor types collected from The Cancer Genome Atlas (TCGA), and the experimental results indicate that our methods are (i) extensible to different tumor types; (ii) robust in the presence of wide technical and biological variations; (iii) invariant to different nuclear segmentation strategies; and (iv) scalable with varying training sample size. In addition, our experiments suggest that enforcing sparsity, during the construction of morphometric context, further improves the performance of the system.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2013 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3850786/pdf/nihms480987.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31939922","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

Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus. 利用离散微积分在轮廓补全中结合用户交互和拓扑约束。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-01 Epub Date: 2013-10-03 DOI: 10.1109/cvpr.2013.246

Jia Xu, Maxwell D Collins, Vikas Singh

引用次数: 14

Computing Diffeomorphic Paths for Large Motion Interpolation. 计算大运动插值的微分同构路径。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2013-06-01 DOI: 10.1109/CVPR.2013.162

Dohyung Seo, Ho Jeffrey, Baba C Vemuri

{"title":"Computing Diffeomorphic Paths for Large Motion Interpolation.","authors":"Dohyung Seo, Ho Jeffrey, Baba C Vemuri","doi":"10.1109/CVPR.2013.162","DOIUrl":"https://doi.org/10.1109/CVPR.2013.162","url":null,"abstract":"In this paper, we introduce a novel framework for computing a path of diffeomorphisms between a pair of input diffeomorphisms. Direct computation of a geodesic path on the space of diffeomorphisms Diff(Ω) is difficult, and it can be attributed mainly to the infinite dimensionality of Diff(Ω). Our proposed framework, to some degree, bypasses this difficulty using the quotient map of Diff(Ω) to the quotient space Diff(M)/Diff(M) μ obtained by quotienting out the subgroup of volume-preserving diffeomorphisms Diff(M) μ . This quotient space was recently identified as the unit sphere in a Hilbert space in mathematics literature, a space with well-known geometric properties. Our framework leverages this recent result by computing the diffeomorphic path in two stages. First, we project the given diffeomorphism pair onto this sphere and then compute the geodesic path between these projected points. Second, we lift the geodesic on the sphere back to the space of diffeomerphisms, by solving a quadratic programming problem with bilinear constraints using the augmented Lagrangian technique with penalty terms. In this way, we can estimate the path of diffeomorphisms, first, staying in the space of diffeomorphisms, and second, preserving shapes/volumes in the deformed images along the path as much as possible. We have applied our framework to interpolate intermediate frames of frame-sub-sampled video sequences. In the reported experiments, our approach compares favorably with the popular Large Deformation Diffeomorphic Metric Mapping framework (LDDMM).","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2013 ","pages":"1227-1232"},"PeriodicalIF":0.0,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2013.162","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32786248","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: 9

Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso. 基于空间约束转导套索的CT图像前列腺分割。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2013-01-01 DOI: 10.1109/CVPR.2013.289

Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen

{"title":"Prostate Segmentation in CT Images via Spatial-Constrained Transductive Lasso.","authors":"Yinghuan Shi, Shu Liao, Yaozong Gao, Daoqiang Zhang, Yang Gao, Dinggang Shen","doi":"10.1109/CVPR.2013.289","DOIUrl":"https://doi.org/10.1109/CVPR.2013.289","url":null,"abstract":"Accurate prostate segmentation in CT images is a significant yet challenging task for image guided radiotherapy. In this paper, a novel semi-automated prostate segmentation method is presented. Specifically, to segment the prostate in the current treatment image, the physician first takes a few seconds to manually specify the first and last slices of the prostate in the image space. Then, the prostate is segmented automatically by the proposed two steps: (i) The first step of prostate-likelihood estimation to predict the prostate likelihood for each voxel in the current treatment image, aiming to generate the 3-D prostate-likelihood map by the proposed Spatial-COnstrained Transductive LassO (SCOTO); (ii) The second step of multi-atlases based label fusion to generate the final segmentation result by using the prostate shape information obtained from the planning and previous treatment images. The experimental result shows that the proposed method outperforms several state-of-the-art methods on prostate segmentation in a real prostate CT dataset, consisting of 24 patients with 330 images. Moreover, it is also clinically feasible since our method just requires the physician to spend a few seconds on manual specification of the first and last slices of the prostate.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2013.289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31953093","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: 34

Selective Transfer Machine for Personalized Facial Action Unit Detection. 个性化面部动作单元检测的选择性转移机。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2013-01-01 DOI: 10.1109/CVPR.2013.451

Wen-Sheng Chu, Fernando De la Torre, Jeffery F Cohn

{"title":"Selective Transfer Machine for Personalized Facial Action Unit Detection.","authors":"Wen-Sheng Chu, Fernando De la Torre, Jeffery F Cohn","doi":"10.1109/CVPR.2013.451","DOIUrl":"https://doi.org/10.1109/CVPR.2013.451","url":null,"abstract":"Automatic facial action unit (AFA) detection from video is a long-standing problem in facial expression analysis. Most approaches emphasize choices of features and classifiers. They neglect individual differences in target persons. People vary markedly in facial morphology (e.g., heavy versus delicate brows, smooth versus deeply etched wrinkles) and behavior. Individual differences can dramatically influence how well generic classifiers generalize to previously unseen persons. While a possible solution would be to train person-specific classifiers, that often is neither feasible nor theoretically compelling. The alternative that we propose is to personalize a generic classifier in an unsupervised manner (no additional labels for the test subjects are required). We introduce a transductive learning method, which we refer to Selective Transfer Machine (STM), to personalize a generic classifier by attenuating person-specific biases. STM achieves this effect by simultaneously learning a classifier and re-weighting the training samples that are most relevant to the test subject. To evaluate the effectiveness of STM, we compared STM to generic classifiers and to cross-domain learning methods in three major databases: CK+ [20], GEMEP-FERA [32] and RU-FACS [2]. STM outperformed generic classifiers in all.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2013 ","pages":"3515-3522"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2013.451","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32685764","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: 288

Sasaki Metrics for Analysis of Longitudinal Data on Manifolds. 流形纵向数据分析的Sasaki度量。

Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 2012-06-01 DOI: 10.1109/CVPR.2012.6247780

Prasanna Muralidharan, P Thomas Fletcher

{"title":"Sasaki Metrics for Analysis of Longitudinal Data on Manifolds.","authors":"Prasanna Muralidharan, P Thomas Fletcher","doi":"10.1109/CVPR.2012.6247780","DOIUrl":"https://doi.org/10.1109/CVPR.2012.6247780","url":null,"abstract":"Longitudinal data arises in many applications in which the goal is to understand changes in individual entities over time. In this paper, we present a method for analyzing longitudinal data that take values in a Riemannian manifold. A driving application is to characterize anatomical shape changes and to distinguish between trends in anatomy that are healthy versus those that are due to disease. We present a generative hierarchical model in which each individual is modeled by a geodesic trend, which in turn is considered as a perturbation of the mean geodesic trend for the population. Each geodesic in the model can be uniquely parameterized by a starting point and velocity, i.e., a point in the tangent bundle. Comparison between these parameters is achieved through the Sasaki metric, which provides a natural distance metric on the tangent bundle. We develop a statistical hypothesis test for differences between two groups of longitudinal data by generalizing the Hotelling T 2 statistic to manifolds. We demonstrate the ability of these methods to distinguish differences in shape changes in a comparison of longitudinal corpus callosum data in subjects with dementia versus healthily aging controls.","PeriodicalId":74560,"journal":{"name":"Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition","volume":"2012 ","pages":"1027-1034"},"PeriodicalIF":0.0,"publicationDate":"2012-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/CVPR.2012.6247780","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32925286","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: 48