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In medical images, the edges of organs are often blurred and unclear. Existing semi-supervised image segmentation methods rarely model edges explicitly. Thus most methods produce inaccurate predictions in target edge regions. In this paper, we propose a contour-aware consistency framework for semi-supervised medical image segmentation. The framework consists of a shared encoder, a vanilla primary decoder and a contour-enhanced auxiliary decoder. The contour-enhanced decoder is designed to enhance the features of the target contour region. The predictions from the primary decoder and the auxiliary decoder are combined to create pseudo labels, enabling the unlabeled data for supervision. For the inconsistent regions in predictions, we propose a self-contrast strategy that further improves the performance by reducing the discrepancy of the dual decoder for the same pixel. We conducted extensive experiments on three publicly available datasets and verified that our approach outperforms other methods for boundary quality. Specifically, with 5% labeled data on Left Atrial (LA) dataset, our proposed approach achieved a Boundary IoU 3.76% higher than the state-of-the-art methods. Code is available at https://github.com/SmileJET/CAC4SSL. • A contour-aware consistency method is developed for semi-supervised medical image segmentation. • A contour-enhanced decoder is designed to ensure anatomy during the training. • A self-contrast strategy is introducted to reduce uncertain regions in the segmentation results. • Extensive experiments on three medical datasets demonstrated the superiority of our method. [ABSTRACT FROM AUTHOR]

Deep learning technology has been widely used in object detection. Although the deep learning technology greatly improves the accuracy of object detection, we also have the challenge of a high computational time. You Only Look Once (YOLO) is a network for object detection in images. In this paper, we propose a real-time object detection algorithm for videos based on the YOLO network. We eliminate the influence of the image background by image preprocessing, and then we train the Fast YOLO model for object detection to obtain the object information. Based on the Google Inception Net (GoogLeNet) architecture, we improve the YOLO network by using a small convolution operation to replace the original convolution operation, which can reduce the number of parameters and greatly shorten the time for object detection. Our Fast YOLO algorithm can be applied to real-time object detection in video. [ABSTRACT FROM AUTHOR]

Automatic multi-organ segmentation in medical images is crucial for many clinical applications. The art methods have reported promising results but rely on massive annotated data. However, such data is hard to obtain due to the need for considerable expertise. In contrast, obtaining a single-organ dataset is relatively easier, and many well-annotated ones are publicly available. To this end, this work raises the partially supervised problem: can we use these single-organ datasets to learn a multi-organ segmentation model? In this paper, we propose the P a r t i al- and M utual- P rior incorporated framework (PRIMP) to learn a robust multi-organ segmentation model by deriving knowledge from single-organ datasets. Unlike existing methods that largely ignore the organs' anatomical prior knowledge, our PRIMP is designed with two key prior shared across different subjects and datasets: (1) partial-prior, each organ has its own character (e.g. , size and shape) and (2) mutual-prior, the relative position between different organs follows the comparatively fixed anatomical structure. Specifically, we propose to incorporate partial-prior of each organ by learning from the single-organ statistics, and inject mutual-prior of organs by learning from the multi-organ statistics. By doing so, the model is encouraged to capture organs' anatomical invariance across different subjects and datasets, thus guaranteeing the anatomical reasonableness of the predictions, narrowing down the problem of domain gaps, capturing spatial information among different slices, thereby improving organs' segmentation performance. Experiments on four publicly available datasets (LiTS, Pancreas, KiTS, BTCV) show that our PRIMP can improve the performance on both the multi-organ and single-organ datasets (17.40% and 3.06% above the baseline model on DSC, respectively) and can surpass the comparative approaches. • We propose PRIMP to learn multi-organ segmentation model from single-organ datasets. • PRIMP can effectively capture anatimical priors of single- and multi-organ. • Experiments on four publicly available datasets show PRIMP's effectiveness. [ABSTRACT FROM AUTHOR]

Abstract: Intrusion detection using sequential pattern mining is a research focusing on the field of information security. This paper first introduces several common sequential pattern mining algorithms, and then expounds its current development with comparisons about the merits and shortcomings with the current mainstream technologies, at the same time, presenting some further discussion upon the future direction of the sequential pattern mining algorithms based intrusion detection. [Copyright &y& Elsevier]

Abstract: Based on the beam coherent-polarization (BCP) matrix approach and propagation law of partially coherent beams, analytical propagation equations of partially polarized Gaussian Schell-model (PGSM) beams through an astigmatic lens are derived, which enables us to study the propagation-induced polarization changes and irradiance distributions at any propagation distance of PGSM beams through an astigmatic lens within the framework of the paraxial approximation. Detailed numerical results for a PGSM beam passing through an astigmatic lens are presented. A comparison with the aberration-free case is made, and shows that the astigmatism affects the propagation properties of PGSM beams. [Copyright &y& Elsevier]

Recovering facial albedo from low quality face images is a challenging task which arises when face recognition is attempted in the wild. Low quality of facial images is usually caused by extrinsic factors such as low resolution and noises, and intrinsic ones such as expressions. Existing research recovers facial albedo by dealing with the extrinsic and intrinsic factors separately. However, it is more natural and potentially more useful to approach albedo recovery by removing the two effects simultaneously. In this paper, we present a novel framework which can recover facial albedo by jointly solving these for both the extrinsic and intrinsic sources of uncertainty. This framework models albedo recovery problem by a joint optimization process which alternatively (1) removes intra-personal variations and (2) performs super resolution. To deal with the intrinsic sources of albedo variability, we use a linear model. To handle extrinsic problems associated with low quality imaging, we use a sparse coding method which is applied to super resolution. The proposed method can also significantly improve the performance of face recognition and clustering in case of very low resolution and in the presence of various facial variations. Extensive experiments and comparisons are conducted on the AR and FERET face databases. Experimental results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Considering networks' heterogeneity in embedding methods is effective. • Triangle motifs preserve network heterogeneity and link directionality. • Atomic-level motif embedding overcomes manual intervention. • Higher-order heterogeneous connectivity patterns cope cold-start well. In recent years, there has been a surge of interest in applying machine learning to graphs and networks that already exist in the world around us. The approach has been successfully used for domains as diverse as traffic management, e-commerce recommendation and public opinion monitoring. A critical aspect of representation learning for applied machine learning is feature engineering. Deep learning-based research in representation learning has developed methods for automatically learning a large number of potentially correlated features from original networks. However, most of these methods cannot be applied to heterogeneous networks, which are true expressions of the real-world. This is because they do not adequately capture the structure and semantics of different types of nodes in heterogeneous networks and the links between them. They also struggle to represent higher-order heterogeneous patterns of connection. This paper proposes a generalized motif-based higher-order representation learning method, MBRep, that learns triangle motif embedding in a network, on the basis of which it can obtain the embedding and representation of nodes in a heterogeneous network. Statistically, significant motif structures are extracted from the original heterogeneous network and combined with the heterogeneity of the nodes. A weight-biased random walk is then applied to the motif level higher-order network, using a SkipGram model to embed the motifs. After this, the embedding of the original network nodes is calculated using weighted averages and feature alignment. This can then be used for link prediction. We confirmed the effectiveness of MBRep by comparing its AUC and MRR performance with other state-of-the-art methods on three real-world datasets. Its adaptability was also validated by conducting a cold-start test. [ABSTRACT FROM AUTHOR]

• Replace hard codematrix with soft codematrix. • Use the regression outputs of the base classifiers to replace the traditional classification outputs. • Iteratively softens codematrix elements using global loss tuning. • Conveniently merge the proposed algorithm into other ECOC algorithms to further improve performance. The technique of error-correcting output codes (ECOC) has been proven to be of high discriminative ability in many classification applications. However, most algorithms on the ECOC were designed based on the binary or ternary codes (referred to as the hard codes), which might fail to precisely correct errors in dealing with tough tasks. In this study, a Soft-Recoding strategy based on a self-adaptive algorithm is proposed, which replaces the traditional hard codes with the real-value elements to better fit the output distribution of the base learners. This is achieved by minimizing the ratio of two distances: the distance of the output vector to the ground-truth class, and the average distance of the output vector to the remaining classes. Extensive experiments using five different hard ECOC algorithms and the corresponding softened versions on twenty datasets with diversified numbers of features and classes confirm the effectiveness of our Soft-Recoding strategy in promoting the performance of the original ECOC algorithms. Our source code and additional results are available at: github.com/MLDMXM2017/SA-soft-recoding. [ABSTRACT FROM AUTHOR]

Highlights • We proposed a novel ECOC algorithm for multiclass microarray data classification based on the data complexity theory. • Various data complexity measures are deployed to detect the intrinsic characteristics of microarray data sets, so as to produce diverse coding matrices. • A new data complexity measure, named as C1, is designed to evaluate data distribution. It benefits the optimization process of our class partition. • The proposed ECOC algorithm performs more stably in most multiclass microarray data sets compared with other popular ECOC algorithms. Abstract Nowadays, a lot of new classification and clustering techniques have been proposed for microarray data analysis. However, the multiclass microarray data classification is still regarded as a tough task because of the small sample size problem and the class imbalance problem. In this paper, we propose a novel error correcting output code (ECOC) algorithm for the classification of multiclass microarray data based on the data complexity (DC) theory. In this algorithm, an ECOC coding matrix is generated based on a hierarchical partition of the class space with the aim of Minimizing Data Complexity (named as ECOC-MDC). As the partition process can be mapped as a binary tree, a compact ensemble with high discrimination power is produced. The performance of ECOC-MDC is compared with some state-of-art ECOC algorithms on six multiclass microarray data sets, and it is found that the proposed algorithm can obtain better results in most cases. The correlation between DC measures and the dichotomizers' performances is checked, and the observations confirm that high complexity in data usually leads to high error rates of the connected dichotomizers. But the error correcting mechanism in the ECOC framework can effectively improve our algorithm's generalization ability. In short, ECOC-MDC can produce a compact ensemble system with high error correction capability through the application of diverse DC measures. Our Matlab code is available at: github.com/MLDMXM2017/ECOC-MDC. [ABSTRACT FROM AUTHOR]

Abstract Endoscopic ultrasonography (EUS) has shown great advantages in the diagnosis and staging of gastrointestinal malignant tumors. However, EUS based diagnosis is limited by variability in the examiner's subjective interpretation to differentiate between normal and early esophageal carcinoma. In this paper, we propose a novel approach aiming at automatic abnormal region detection from the esophageal EUS images; the contribution is three-fold: first, we present a series of preprocessing strategies developed specifically for the enhancement of EUS images to aid the estimation in the subsequent works. Second, we provide an automatic layer segmentation method based on the multiple surface graph searching approach with incorporation of geometric constraints, which is applied to segment the EUS images into five discernible layers. Third, we introduce the novel feature extraction strategy by utilizing the features from each column in the segmented layers. The SVM classifier is then applied to fulfill the normal and early esophageal carcinoma classification. Subsequently, a clustering method is used to assemble the abnormal columns together so as to detect the abnormal region. Experimental results show that our method has demonstrated its robustness even facing noisy EUS images, and has achieved high accuracy in detecting abnormal region. [ABSTRACT FROM AUTHOR]