The Experts below are selected from a list of 5073 Experts worldwide ranked by ideXlab platform
Dinggang Shen - One of the best experts on this subject based on the ideXlab platform.
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progressive multi atlas label fusion by dictionary evolution
Medical Image Analysis, 2017Co-Authors: Quansen Sun, Dinggang Shen, Yantao Song, Khosro BahramiAbstract:Accurate segmentation of anatomical structures in medical images is important in recent imaging based studies. In the past years, multi-atlas patch-based label fusion methods have achieved a great success in medical image segmentation. In these methods, the appearance of each input image patch is first represented by an atlas patch dictionary (in the image domain), and then the latent label of the input image patch is predicted by applying the estimated representation coefficients to the corresponding anatomical labels of the atlas patches in the atlas label dictionary (in the label domain). However, due to the generally large gap between the patch appearance in the image domain and the patch structure in the label domain, the estimated (patch) representation coefficients from the image domain may not be optimal for the final label fusion, thus reducing the labeling accuracy. To address this issue, we propose a novel label fusion framework to seek for the suitable label fusion weights by progressively constructing a dynamic dictionary in a layer-by-layer manner, where the intermediate dictionaries act as a sequence of guidance to steer the transition of (patch) representation coefficients from the image domain to the label domain. Our proposed multi-layer label fusion framework is flexible enough to be applied to the existing labeling methods for improving their label fusion performance, i.e., by extending their single-layer static dictionary to the multi-layer dynamic dictionary. The experimental results show that our proposed progressive label fusion method achieves more accurate hippocampal segmentation results for the ADNI dataset, compared to the counterpart methods using only the single-layer static dictionary.
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hierarchical label fusion with multiscale feature representation and label specific patch partition
Medical Image Computing and Computer-Assisted Intervention, 2014Co-Authors: Dinggang ShenAbstract:Recently, patch-based label fusion methods have achieved many successes in medical imaging area. After registering atlas images to the target image, the label at each target image point can be subsequently determined by checking the patchwise similarities between the underlying target image patch and all atlas image patches. Apparently, the definition of patchwise similarity is critical in label fusion. However, current methods often simply use entire image patch with fixed patch size throughout the entire label fusion procedure, which could be insufficient to distinguish complex shape/appearance patterns of anatomical structures in medical imaging scenario. In this paper, we address the above limitations at three folds. First, we assign each image patch with multiscale feature representations such that both local and semi-local image information can be encoded to increase robustness of measuring patchwise similarity in label fusion. Second, since multiple variable neighboring structures could present in one image patch, simply computing patchwise similarity based on the entire image patch is not specific to the particular structure of interest under labeling and can be easily misled by the surrounding variable structures in the same image patch. Thus, we partition each atlas patch into a set of new label-specific atlas patches according to the existing label information in the atlas images. Then, the new label-specific atlas patches can be more specific and flexible for label fusion than using the entire image patch, since the complex image patch has now been semantically divided into several distinct patterns. Finally, in order to correct the possible mis-labeling, we hierarchically improve the label fusion result in a coarse-to-fine manner by iteratively repeating the label fusion procedure with the gradually-reduced patch size. More accurate label fusion results have been achieved by our hierarchical label fusion method with multiscale feature presentations upon label-specific atlas patches.
Zhi-hua Zhou - One of the best experts on this subject based on the ideXlab platform.
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multi label optimal margin distribution machine
Machine Learning, 2020Co-Authors: Zhihao Tan, Peng Tan, Yuan Jiang, Zhi-hua ZhouAbstract:Multi-label support vector machine (Rank-SVM) is a classic and effective algorithm for multi-label classification. The pivotal idea is to maximize the minimum margin of label pairs, which is extended from SVM. However, recent studies disclosed that maximizing the minimum margin does not necessarily lead to better generalization performance, and instead, it is more crucial to optimize the margin distribution. Inspired by this idea, in this paper, we first introduce margin distribution to multi-label learning and propose multi-label Optimal margin Distribution Machine (mlODM), which optimizes the margin mean and variance of all label pairs efficiently. Extensive experiments in multiple multi-label evaluation metrics illustrate that mlODM outperforms SVM-style multi-label methods. Moreover, empirical study presents the best margin distribution and verifies the fast convergence of our method.
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a k nearest neighbor based algorithm for multi label classification
Granular Computing, 2005Co-Authors: Min Ling Zhang, Zhi-hua ZhouAbstract:In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi-label lazy learning approach named ML-kNN is presented, which is derived from the traditional k-nearest neighbor (kNN) algorithm. In detail, for each new instance, its k-nearest neighbors are firstly identified. After that, according to the label sets of these neighboring instances, maximum a posteriori (MAP) principle is utilized to determine the label set for the new instance. Experiments on a real-world multi-label bioinformatic data show that ML-kNN is highly comparable to existing multi-label learning algorithms.
Daewon Kim - One of the best experts on this subject based on the ideXlab platform.
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mutual information based multi label feature selection using interaction information
Expert Systems With Applications, 2015Co-Authors: Jaesung Lee, Daewon KimAbstract:We proposed a MI-based feature selection method without problem transformation.A score function measuring dependency between features and labels was derived.We derived theoretical bounds of score function.Based on theoretical bounds, a score function of variations from MI was chosen. Multi-label feature selection is regarded as one of the most promising techniques that can be used to maximize the efficacy and efficiency of multi-label classification. However, because multi-label feature selection algorithms must consider multiple labels concurrently, the task is more difficult than single-label feature selection tasks. In this paper, we propose the Mutual Information-based multi-label feature selection method using interaction information. This method is naturally able to measure dependencies among multiple variables. To develop an efficient multi-label feature selection method, we derive theoretical bounds for the interaction information. Empirical studies indicate that our proposed multi-label feature selection method discovers effective feature subsets for multi-label classification problems.
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feature selection for multi label classification using multivariate mutual information
Pattern Recognition Letters, 2013Co-Authors: Jaesung Lee, Daewon KimAbstract:Recently, classification tasks that naturally emerge in multi-label domains, such as text categorization, automatic scene annotation, and gene function prediction, have attracted great interest. As in traditional single-label classification, feature selection plays an important role in multi-label classification. However, recent feature selection methods require preprocessing steps that transform the label set into a single label, resulting in subsequent additional problems. In this paper, we propose a feature selection method for multi-label classification that naturally derives from mutual information between selected features and the label set. The proposed method was applied to several multi-label classification problems and compared with conventional methods. The experimental results demonstrate that the proposed method improves the classification performance to a great extent and has proved to be a useful method in selecting features for multi-label classification problems.
Min Ling Zhang - One of the best experts on this subject based on the ideXlab platform.
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leveraging implicit relative labeling importance information for effective multi label learning
International Conference on Data Mining, 2015Co-Authors: Min Ling Zhang, Xin GengAbstract:In multi-label learning, each training example is represented by a single instance while associated with multiple labels, and the task is to predict a set of relevant labels for the unseen instance. Existing approaches learn from multi-label data by assuming equal labeling-importance, i.e. all the associated labels are regarded to be relevant while their relative importance for the training example are not differentiated. Nonetheless, this assumption fails to reflect the fact that the importance degree of each associated label is generally different, though the importance information is not explicitly accessible from the training examples. In this paper, we show that effective multi-label learning can be achieved by leveraging the implicit relative labeling-importance (RLI) information. Specifically, RLI degrees are formalized as multinomial distribution over the label space, which are estimated by adapting an iterative label propagation procedure. After that, the multi-label prediction model is learned by fitting the estimated multinomial distribution as regularized with popular multi-label empirical loss. Comprehensive experiments clearly validate the usefulness of leveraging implicit RLI information to learn from multi-label data.
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solving the partial label learning problem an instance based approach
International Conference on Artificial Intelligence, 2015Co-Authors: Min Ling ZhangAbstract:In partial label learning, each training example is associated with a set of candidate labels, among which only one is valid. An intuitive strategy to learn from partial label examples is to treat all candidate labels equally and make prediction by averaging their modeling outputs. Nonetheless, this strategy may suffer from the problem that the modeling output from the valid label is overwhelmed by those from the false positive labels. In this paper, an instance-based approach named IPAL is proposed by directly disambiguating the candidate label set. Briefly, IPAL tries to identify the valid label of each partial label example via an iterative label propagation procedure, and then classifies the unseen instance based on minimum error reconstruction from its nearest neighbors. Extensive experiments show that IPAL compares favorably against the existing instance-based as well as other state-of-the-art partial label learning approaches.
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multi label learning by exploiting label dependency
Knowledge Discovery and Data Mining, 2010Co-Authors: Min Ling ZhangAbstract:In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example. Due to the tremendous (exponential) number of possible label sets, the task of learning from multi-label examples is rather challenging. Therefore, the key to successful multi-label learning is how to effectively exploit correlations between different labels to facilitate the learning process. In this paper, we propose to use a Bayesian network structure to efficiently encode the conditional dependencies of the labels as well as the feature set, with the feature set as the common parent of all labels. To make it practical, we give an approximate yet efficient procedure to find such a network structure. With the help of this network, multi-label learning is decomposed into a series of single-label classification problems, where a classifier is constructed for each label by incorporating its parental labels as additional features. Label sets of unseen examples are predicted recursively according to the label ordering given by the network. Extensive experiments on a broad range of data sets validate the effectiveness of our approach against other well-established methods.
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a k nearest neighbor based algorithm for multi label classification
Granular Computing, 2005Co-Authors: Min Ling Zhang, Zhi-hua ZhouAbstract:In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set whose size is unknown a priori for each unseen instance. In this paper, a multi-label lazy learning approach named ML-kNN is presented, which is derived from the traditional k-nearest neighbor (kNN) algorithm. In detail, for each new instance, its k-nearest neighbors are firstly identified. After that, according to the label sets of these neighboring instances, maximum a posteriori (MAP) principle is utilized to determine the label set for the new instance. Experiments on a real-world multi-label bioinformatic data show that ML-kNN is highly comparable to existing multi-label learning algorithms.
Barton J Scott - One of the best experts on this subject based on the ideXlab platform.
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how to defame a god public selfhood in the maharaj Libel case
South Asia-journal of South Asian Studies, 2015Co-Authors: Barton J ScottAbstract:This article argues that competing ideas about the nature of public selfhood structured the Maharaj Libel Case, as well as colonial publics more broadly. Jadunathji Maharaj had, in effect, lost his Libel suit even before it went to court. For Libel law, the essence of the human person is a private self that owns various forms of property, including the public persona known as ‘reputation’. For the Hindu Pushtimarg, meanwhile, the Maharaj was considered an incarnation of Krishna; his religious or public self preceded and was the ontological ground for his merely personal being. To compare these two conceptualisations of the self is to see how selfhood became an important site for the articulation of the public in colonial India.