Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features...
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ISBN:
(数字)9783031438981
ISBN:
(纸本)9783031438974;9783031438981
Anomaly detection in medical imaging is a challenging task in contexts where abnormalities are not annotated. This problem can be addressed through unsupervised anomaly detection (UAD) methods, which identify features that do not match with a reference model of normal profiles. Artificial neural networks have been extensively used for UAD but they do not generally achieve an optimal trade-off between accuracy and computational demand. As an alternative, we investigate mixtures of probability distributions whose versatility has been widely recognized for a variety of data and tasks, while not requiring excessive design effort or tuning. Their expressivity makes them good candidates to account for complex multivariate reference models. Their much smaller number of parameters makes them more amenable to interpretation and efficient learning. However, standard estimation procedures, such as the Expectation-Maximization algorithm, do not scale well to large data volumes as they require high memory usage. To address this issue, we propose to incrementally compute inferential quantities. This online approach is illustrated on the challenging detection of subtle abnormalities in MR brain scans for the follow-up of newly diagnosed Parkinsonian patients. The identified structural abnormalities are consistent with the disease progression, as accounted by the Hoehn and Yahr scale.
Crowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags ...
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Crowdsourced data in science might be severely error-prone due to the inexperience of annotators participating in the project. In this work, we present a procedure to detect specific structures in an image given tags provided by multiple annotators and collected through a crowdsourcing methodology. The procedure consists of two stages based on the Expectation-Maximization (em) algorithm, one for clustering and the other one for detection, and it gracefully combines data coming from annotators with unknown reliability in an unsupervised manner. An online implementation of the approach is also presented that is well suited to crowdsourced streaming data. Comprehensive experimental results with real data from the MalariaSpot project are also included. (C) 2018 Elsevier Ltd. All rights reserved.
This paper introduces a novel algorithm for the online estimate of the Riemannian mixture model parameters. This new approach counts on Riemannian geometry concepts to extend the well-known Titterington approach for t...
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ISBN:
(纸本)9783319684451;9783319684444
This paper introduces a novel algorithm for the online estimate of the Riemannian mixture model parameters. This new approach counts on Riemannian geometry concepts to extend the well-known Titterington approach for the online estimate of mixture model parameters in the Euclidean case to the Riemannian manifolds. Here, Riemannian mixtures in the Riemannian manifold of Symmetric Positive Definite (SPD) matrices are analyzed in details, even if the method is well suited for other manifolds.
The naive Bayes model has proven to be a simple yet effective model, which is very popular for pattern recognition applications such as data classification and clustering. This paper explores the possibility of using ...
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The naive Bayes model has proven to be a simple yet effective model, which is very popular for pattern recognition applications such as data classification and clustering. This paper explores the possibility of using this model for multidimensional data visualization. To achieve this, a new learning algorithm called naive Bayes self-organizing map (NBSOM) is proposed to enable the naive Bayes model to perform topographic mappings. The training is carried out by means of an online expectation maximization algorithm with a self-organizing principle. The proposed method is compared with principal component analysis, self-organizing maps, and generative topographic mapping on two benchmark data sets and a real-world image processing application. Overall, the results show the effectiveness of NBSOM for multidimensional data visualization.
This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Ti...
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ISBN:
(纸本)9780769549132;9781467346511
This work proposes a fast background learning algorithm for foreground detection under changing illumination. Gaussian Mixture Model (GMM) is an effective statistical model in background learning. We first focus on Titterington's online em algorithm that can be used for real-time unsupervised GMM learning, and then advocate a deterministic data assignment strategy to avoid Bayesian computation. The color of the foreground is apt to be influenced by the environmental illumination that usually produce undesirable effect for GMM updating;however, a collinear feature of pixel intensity under changing light is discovered in RGB color space. This feature is afterward used as a reliable clue to decide which part of mixture to update under changing light. A foreground detection step proposed in early version of this work is employed to extract foreground objects by comparing the estimated background model with the current video frame. Experiments have shown the proposed method is able to achieve satisfactory static background images of scenes as well as is also superior to some mainstream methods in detection performance under both indoor and outdoor scenes.
Humans can generate accurate and appropriate motor commands in various, and even uncertain, environments. MOSAIC (MOdular Selection And Identifi cation for Control) was originally proposed to describe this human abili...
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Humans can generate accurate and appropriate motor commands in various, and even uncertain, environments. MOSAIC (MOdular Selection And Identifi cation for Control) was originally proposed to describe this human ability, but this model is hard to analyze mathematically because of its emphasis on biological plausibility. In this article, we present an alternative and probabilistic model of MOSAIC (p-MOSAIC) as a mixture of normal distributions and an onlineem-based learning method for its predictors and controllers. A theoretical consideration shows that the learning rule of p-MOSAIC corresponds to that of MOSAIC except for some points which are mostly related to the learning of controllers. The results of experiments using synthetic datasets demonstrate some practical advantages of p-MOSAIC. One is that the learning rule of pMOSAIC stabilizes the estimation of "responsibility." Another is that p-MOSAIC realizes more accurate control and robust parameter learning in comparison to the original MOSAIC, especially in noisy environments, due to the direct incorporation of the noises into the model.
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