Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging. The main motivation is to capture ...most relevant information from sources into a single output, which plays an important role in medical diagnosis. In this paper, a novel fusion framework is proposed for multimodal medical images based on non-subsampled contourlet transform (NSCT). The source medical images are first transformed by NSCT followed by combining low- and high-frequency components. Two different fusion rules based on phase congruency and directive contrast are proposed and used to fuse low- and high-frequency coefficients. Finally, the fused image is constructed by the inverse NSCT with all composite coefficients. Experimental results and comparative study show that the proposed fusion framework provides an effective way to enable more accurate analysis of multimodality images. Further, the applicability of the proposed framework is carried out by the three clinical examples of persons affected with Alzheimer, subacute stroke and recurrent tumor.
Fully connected representation learning (FCRL) is one of the widely used network structures in multimodel image classification frameworks. However, most FCRL-based structures, for instance, stacked ...autoencoder encode features and find the final cognition with separate building blocks, resulting in loosely connected feature representation. This article achieves a robust representation by considering a low-dimensional feature and the classifier model simultaneously. Thus, a new hierarchical subnetwork-based neural network (HSNN) is proposed in this article. The novelties of this framework are as follows: 1) it is an iterative learning process, instead of stacking separate blocks to obtain the discriminative encoding and the final classification results. In this sense, the optimal global features are generated; 2) it applies Moore-Penrose (MP) inverse-based batch-by-batch learning strategy to handle large-scale data sets, so that large data set, such as Place365 containing 1.8 million images, can be processed effectively. The experimental results on multiple domains with a varying number of training samples from <inline-formula> <tex-math notation="LaTeX">{\sim } 1 \,\,K </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">{\sim }2~M </tex-math></inline-formula> show that the proposed feature reinforcement framework achieves better generalization performance compared with most state-of-the-art FCRL methods.
Abstract
Constrained by the Nielsen-Ninomiya no-go theorem, in all so-far experimentally determined Weyl semimetals (WSMs) the Weyl points (WPs) always appear in pairs in the momentum space with no ...exception. As a consequence, Fermi arcs occur on surfaces which connect the projections of the WPs with opposite chiral charges. However, this situation can be circumvented in the case of unpaired WP, without relevant surface Fermi arc connecting its surface projection, appearing singularly, while its Berry curvature field is absorbed by nontrivial charged nodal walls. Here, combining angle-resolved photoemission spectroscopy with density functional theory calculations, we show experimentally that a singular Weyl point emerges in PtGa at the center of the Brillouin zone (BZ), which is surrounded by closed Weyl nodal walls located at the BZ boundaries and there is no Fermi arc connecting its surface projection. Our results reveal that nontrivial band crossings of different dimensionalities can emerge concomitantly in condensed matter, while their coexistence ensures the net topological charge of different dimensional topological objects to be zero. Our observation extends the applicable range of the original Nielsen-Ninomiya no-go theorem which was derived from zero dimensional paired WPs with opposite chirality.
Due to the lack of pre-judgment of fingerprints, fingerprint authentication systems are frequently vulnerable to artificial replicas. Anonymous people can impersonate authorized users to complete ...various authentication operations, thereby disrupting the order of life and causing tremendous economic losses to society. Therefore, to ensure that authorized users' fingerprint information is not used illegally, one possible anti-spoofing technique, called fingerprint liveness detection (FLD), has been exploited. Compared with the hand-crafted feature methods, the deep convolutional neural network (DCNN) can automatically learn the high-level semantic detail via supervised learning algorithm without any professional background knowledge. However, one disadvantage of most CNNs models is that fixed scale images (e.g., <inline-formula> <tex-math notation="LaTeX">227\times 227 </tex-math></inline-formula>) are essential in the input layer. Although the scale problem can be handled by cropping or scaling operations via transforming an image of any scale into a fixed scale, they can easily cause some key texture information loss and image resolution degradation, which will weaken the generalization performance of the classifier model. In this paper, a novel FLD method called an improved DCNN with image scale equalization, has been proposed to preserve texture information and maintain image resolution. Besides, an adaptive learning rate method has been used in this paper. In the performance evaluation, the confusion matrix is applied into FLD for the first time as a performance indicator. The amounts of the experimental results based on the LivDet 2011 and LivDet 2013 data sets also verify that the detection performance of our method is superior to other methods.
The Wnt/β-catenin pathway has important roles in chemoresistance and multidrug resistance 1 (MDR1) expression in some cancers, but its involvement in breast cancer and the underlying molecular ...mechanism are undefined. In this study, we demonstrated that the Wnt/β-catenin pathway is activated in chemoresistant breast cancer cells. Using a Wnt pathway-specific PCR array screening assay, we detected that Pygo2, a newly identified Wnt/β-catenin pathway component, was the most upregulated gene in the resistant cells. Additional experiments indicated that Pygo2 activated MDR1 expression in the resistant cells via the Wnt/β-catenin pathway. Moreover, the inhibition of Pygo2 expression restored the chemotherapeutic drug sensitivity of the resistant cells and reduced the breast cancer stem cell population in these cells in response to chemotherapy. Importantly, these activities induced by Pygo2 were mediated by MDR1. We also determined the effect of Pygo2 on the sensitivity of breast tumors resistant to doxorubicin in a mouse model. Finally, RNA samples from 64 paired patient tumors (before and after chemotherapy) highly and significantly overexpressed Pygo2 and/or MDR1 after treatment, thus underlining a pivotal role for the Pygo2-mediated Wnt/β-catenin pathway in the clinical chemoresistance of breast cancer. Our data represent the first implication of the Wnt/β-catenin pathway in breast cancer chemoresistance and identify potential new targets to treat the recurrence of breast cancer.
Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it still suffers from two problems: one is insufficient robustness to image noise, and the other is ...the Euclidean distance in FCM, which is sensitive to outliers. In this paper, we propose two new algorithms, generalized FCM (GFCM) and hierarchical FCM (HFCM), to solve these two problems. Traditional FCM can be considered as a linear combination of membership and distance from the expression of its mathematical formula. GFCM is generated by applying generalized mean on these two items. We impose generalized mean on membership to incorporate local spatial information and cluster information, and on distance function to incorporate local spatial information and image intensity value. Thus, our GFCM is more robust to image noise with the spatial constraints: the generalized mean. To solve the second problem caused by Euclidean distance (l2 norm), we introduce a more flexibility function which considers the distance function itself as a sub-FCM. Furthermore, the sub-FCM distance function in HFCM is general and flexible enough to deal with non-Euclidean data. Finally, we combine these two algorithms to introduce a new generalized hierarchical FCM (GHFCM). Experimental results demonstrate the improved robustness and effectiveness of the proposed algorithm.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Defects on rail surfaces, which have become critical problems, need to be detected and removed as quickly as possible to ensure the fast, safe, and stable operation of trains. At present, although ...many solutions have been proposed to address these problems, the comprehensiveness, rapidity, and accuracy of defect detection remain unsatisfactory. This study aims to resolve these existing problems and accordingly proposes a multi-model rail surface defect detection system based on convolutional neural networks (MRSDI-CNN) from the standpoint of studying the squat on the rail surface. The convolutional neural networks utilized include the improved Single Shot MultiBox Detector (SSD) and You Only Look Once version 3(YOLOv3)-two types of one-stage networks. We expounded and analyzed the performance of the convolutional neural networks as well as their applicability to rail surface defect detection. We used a diverse range of rail defect sizes to improve the detection performance of the two deep learning networks, following which they could identify three types of squats in parallel with improved accuracy and without reduction of the detection speed. The experimental results confirm the effectiveness and superiority of the proposed method over those of previous studies.
Fingerprint-spoofing attack often occurs when imposters gain access illegally by using artificial fingerprints, which are made of common fingerprint materials, such as silicon, latex, etc. Thus, to ...protect our privacy, many fingerprint liveness detection methods are put forward to discriminate fake or true fingerprint. Current work on liveness detection for fingerprint images is focused on the construction of complex handcrafted features, but these methods normally destroy or lose spatial information between pixels. Different from existing methods, convolutional neural network (CNN) can generate high-level semantic representations by learning and concatenating low-level edge and shape features from a large amount of labeled data. Thus, CNN is explored to solve the above problem and discriminate true fingerprints from fake ones in this paper. To reduce the redundant information and extract the most distinct features, ROI and PCA operations are performed for learned features of convolutional layer or pooling layer. After that, the extracted features are fed into SVM classifier. Experimental results based on the LivDet 2013 and the LivDet 2011 datasets, which are captured by using different fingerprint materials, indicate that the classification performance of our proposed method is both efficient and convenient compared with the other previous methods.
Organizing Books and Authors by Multilayer SOM Haijun Zhang; Chow, Tommy W. S.; Wu, Q. M. Jonathan
IEEE transaction on neural networks and learning systems,
12/2016, Letnik:
27, Številka:
12
Journal Article
This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich ...tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.
Aims
This study was conducted to assess the effects of acute heat stress (HS) on intestinal microbiota, and the associations with the changes in feed intake (FI) and serum profile.
Methods and ...Results
Twenty four individually housed pigs (Duroc × Large White × Landrace, 30 ± 1 kg body weight) were randomly assigned to receive one of three treatments (8 pigs/treatment): (i) thermal neutral (TN) conditions (25 ± 1°C), (ii) HS conditions (35 ± 1°C), (iii) pair‐feeding (PF) with HS under TN conditions. After 24‐h treatment, pigs were monitored to assess FI, and samples of serum and faeces were collected to investigate serum profile, microbial composition and short chain fatty acids (SCFAs). The results showed that HS decreased (P < 0·05) FI compared with the TN group. Compared with TN group, HS changed the serum profile by affecting biochemical parameters and hormones related with energy metabolism and stress response; immune indicators were also altered in HS group. Most of changes in serum profile were independent of FI reduction. Additionally, HS shifted the diversity and composition of faecal microbial community by increasing (P < 0·05) Proteobacteria and decreasing (P < 0·05) Bacteroidetes. Moreover, HS decreased (P < 0·05) the concentrations of propionate, butyrate, valerate, iso‐valerate and total SCFAs in faeces in an FI‐independent manner. Furthermore, the Spearman correlation analysis implied that changes of serum profile have potential correlation with alterations of faecal microbiota and their SCFAs metabolites in acute HS‐treated grow‐finishing pigs.
Conclusions
Metabolism disorders caused by 24‐h acute HS associated with changes of faecal microbiota and their SCFAs metabolites in an FI‐independent manner in grow‐finishing pigs.
Significance and Impact of the Study
These results give us a new insight of the intestinal damage caused by acute HS and the underlying mechanisms.
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