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In this paper, finite-time synchronization and H ∞ synchronization of coupled complex-valued memristive neural networks (CCVMNNs) with or without parameter uncertainty are analyzed. First, a finite-time synchronization (FTS) condition is presented for CCVMNNs by means of deploying Lyapunov stability theory and developing suitable controllers. Then, we utilize the similar method to derive a criterion of robust finite-time synchronization (RFTS) for the proposed CCVMNNs with uncertain parameter. Furthermore, we establish some criteria for the sake of ensuring that the considered network can reach finite-time H ∞ synchronization and robust finite-time H ∞ synchronization. At last, two numerical examples with simulations demonstrate the validity of the acquired results. [ABSTRACT FROM AUTHOR]

As manual delineation of lesions in medical image is a very tedious and time consuming process, accurate and automatic segmentation of medical images can assist diagnosis and treatment. In this study, we propose a deep convolution neural network for stroke magnetic resonance imaging(MRI) segmentation. The main structure of our network consists of two symmetrical deep sub-networks, in which dense blocks are embedded for extracting effective features from sparse pixels to alleviate the over-fitting problem of deep networks. We use the multi-kernel to divide the network into two sub-networks for acquiring more receptive fields, and the dropout regularization method to achieve an effective feature mapping. For the post-processing of the soft segmentation, we use image median filtering to alleviate noises and preserve the edge details of images. Our network is evaluated on two public benchmark segmentation challenges (SISS: sub-acute ischemic stroke lesion segmentation and SPES: acute stroke outcome/penumbra estimation) with multi-modality MRI sequences. According to the results of the public benchmark reports, among 9 teams participating in both SISS and SPES challenges at the same time, our network achieves the top performance on SISS challenge, and the top 3 performance on the SPES challenge. In addition, our network also exhibits state-of-the-art performance compared with other segmentation methods. Finally, we extensively evaluate our network with an ablation experiment. The experimental results show that both multi-kernel and dropout strategies can improve the segmentation accuracy of our proposed network. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurological developmental disorder that typically causes impaired communication and compromised social interactions. The current clinical assessment of ASD is typically based on behavioral observations and lack of the understanding of the neurological mechanism and the progression of the brain development. The functional magnetic resonance imaging (fMRI) data is one of the commonly-used imaging modalities for understanding human brain mechanisms as well as the diagnosis and treatment of brain disorders such as ASD. In this paper, we proposed a semi-supervised autoencoder (AE) for autism diagnosis using functional connectivity (FC) pattern obtained from resting-state fMRI. An unsupervised autoencoder in combination with the supervised classification networks enables semi-supervised learning in which an autoencoder for learning hidden features and a neural network based classifier are trained together. Compared to train the autoencoder and classifier in separate phases, the proposed semi-supervised learning essentially helps tune the latent feature representation learning towards the goal of classification, and thus leads to improvements in autism diagnosis performance. The proposed model is evaluated by using cross-validation methods on ABIDE I database. Experimental results demonstrate that the proposed model achieves improved classification performance, and that the proposed semi-supervised learning framework can integrate unlabelled fMRI data for better feature learning and improved classification accuracy. [ABSTRACT FROM AUTHOR]

In modern neuroscience and clinical study, neuroscientists and clinicians often use non-invasive imaging techniques to validate theories and computational models, observe brain activities and diagnose brain disorders. The functional Magnetic Resonance Imaging (fMRI) is one of the commonly-used imaging modalities that can be used to understand human brain mechanisms as well as the diagnosis and treatment of brain disorders. The advances in artificial intelligence and the emergence of deep learning techniques have shown promising results to better interpret fMRI data. Deep learning techniques have rapidly become the state of the art for analyzing fMRI data sets and resulted in performance improvements in diverse fMRI applications. Deep learning is normally presented as an end-to-end learning process and can alleviate feature engineering requirements and hence reduce domain knowledge requirements to some extent. Under the framework of deep learning, fMRI data can be considered as images, time series or images series. Hence, different deep learning models such as convolutional neural networks, recurrent neural network, or a combination of both, can be developed to process fMRI data for different tasks. In this review, we discussed the basics of deep learning methods and focused on its successful implementations for brain disorder diagnosis based on fMRI images. The goal is to provide a high-level overview of brain disorder diagnosis with fMRI images from the perspective of deep learning applications. [ABSTRACT FROM AUTHOR]

• Comparative review on image preprocessing and attribute analysis techniques for malignant liver diagnosis. • Critical analysis of state-of-the-art liver CAD systems. • Collective overview of malignant liver CAD based on US, MRI and CT. The computer-based approach is required for the non-invasive detection of chronic liver diseases that are asymptomatic, progressive, and potentially fatal in nature. In this study, we review the computer-aided diagnosis of hepatic lesions in view of diffuse- and focal liver disorders. This survey mainly focuses on three image acquisition modalities: ultrasonography, computed tomography, and magnetic resonance imaging. We present the insightful analysis with pros and cons for each preliminary step, particularly preprocessing, attribute analysis, and classification techniques to accomplish clinical diagnostic tasks. In preprocessing, we explore and compare commonly used denoising, deblurring and segmentation methods. Denoising is mainly performed with nonlinear models. In contrast, deep neural networks are frequently applied for deblurring and automatic segmentation of region-of-interest. In attribute analysis, the most common approach comprises texture properties. For classification, the support vector machine is mainly utilized across three image acquisition modalities. However, comparative analysis shows the best performance is obtained by deep learning-based convolutional neural networks. Considering biopsy samples or pathological factors such as overall stage, margin, and differentiation can be helpful for improving the prediction performance. In addition, technique breakthrough is expected soon with advances in machine learning models to address data limitation problems and improve the prediction performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a four-neuron bi-directional associative memory (BAM, for short) neural network with two delays. We choose connection weights and the sum of delays as bifurcation parameters and derive the critical values where a double Hopf bifurcation may occur by analyzing the associated characteristic equation which is a fourth-degree polynomial exponential equation. Meanwhile, we obtain some parameter conditions on the existence of invariant 2-tori of the truncated normal form near the bifurcation point by the center manifold theorem and normal form method. Despite the fact that the higher-degree terms may destroy the invariant 2-tori of the truncated normal form, we prove that the neural network model has quasi-periodic invariant 2-tori for most of the parameter set where the truncated normal form possesses invariant 2-tori in a sufficiently small neighborhood of the bifurcation point. Numerical examples and simulations are given to support the theoretical analysis. [ABSTRACT FROM AUTHOR]

The classification of mild cognitive impairment (MCI), which is a early stage of Alzheimer's disease and is associated with brain structural and functional changes, is still a challenging task. Recent studies have shown great promise for improving the performance of MCI classification by combining multiple structural and functional features, such as grey matter volume and clustering coefficient. However, extracting which features and how to combine multiple features to improve the performance of MCI classification have always been challenging problems. To address these problems, in this study we propose a new method to enhance the feature representation of multi-modal MRI data by combining multi-view information to improve the performance of MCI classification. Firstly, we extract two structural features (including grey matter volume and cortical thickness) and two functional features (including clustering coefficient and shortest path length) of each cortical brain region based on automated anatomical labeling (AAL) atlas from both T1w MRI and rs-fMRI data of each subject. Then, in order to obtain features that are more helpful in distinguishing MCI subjects, an improved multi-task feature selection method, namely MTFS-gLASSO-TTR, is proposed. Finally, a multi-kernel learning algorithm is adopted to combine multiple features to perform the MCI classification task. Our proposed MCI classification method is evaluated on 315 subjects (including 105 LMCI subjects, 105 EMCI subjects and 105 NCs) with both T1w MRI and rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that our proposed method achieves an accuracy of 88.5% and an area under the receiver operating characteristic (ROC) curve (AUC) of 0.897 for LMCI/NC classification, an accuracy of 82.7% and an AUC of 0.832 for EMCI/NC classification, and an accuracy of 79.6% and an AUC of 0.803 for LMCI/EMCI classification, respectively. In addition, by comparison, the accuracy and AUC values of our proposed method are better than those of some existing state-of-the-art methods in MCI classification. Overall, our proposed MCI classification method is effective and promising for automatic diagnosis of MCI in clinical practice. [ABSTRACT FROM AUTHOR]

Among machine learning algorithms, Support Vector Machine (SVM) is outstanding for its high efficiency and good generalization ability. This paper mainly concerns the classification performance of SVMs for multiple classes and auxiliary algorithms combined with SVMs. These auxiliary algorithms include Recursive Feature Elimination (RFE) algorithm, parameters optimizing methods and Two-Step Classification strategy. Results are given under data-based framework that classification ability and operation efficiency of SVMs are both improved when dimension is reduced; and Two-Step Classification SVM (TSC-SVM) works well under circumstances that samples overlap with each other seriously. In TSC-SVM, differences between adjacent samples are denoised by wavelet transform and magnified by a proper weighting function, after samples are sorted into correct groups in the first step. Discussions and comparisons are based on abalone dataset. According to the simulations, it is believed that step-wise SVMs have superior classification ability. [ABSTRACT FROM AUTHOR]

Automatic International Classification of Diseases (ICD) coding is an important task in the future of artificial intelligence healthcare. In recent years, a lot of traditional machine learning-based methods have been proposed, and they achieved good results on this task. However, these traditional machine learning-based methods for automatic ICD coding only focus on the semantic features of clinical notes and ignore the feature extraction of ICD titles that are the descriptions of ICD codes. In this paper, we propose a knowledge attention-based deep learning framework called KAICD for automatic ICD coding. KAICD makes full use of the clinic notes and the ICD titles. The semantic features of clinic notes are extracted by a multi-scale convolutional neural network. For ICD titles, we use attention-based Bidirectional Gated Recurrent Unit (Bi-GRU) to build a knowledge database, which can offer additional information. Depending on input clinic notes, we can use the attention mechanism to obtain different knowledge vectors from the knowledge database where some ICD titles are more relevant to the input clinic notes. Last, we concatenate the knowledge vectors and the semantic features of clinic notes, and use them for the final prediction. KAICD is tested on a public dataset Medical Information Mart for Intensive Care III (MIMIC III); it achieves micro-precision of 0.502, micro-recall of 0.428, and micro-f1 of 0.462, which outperforms other competing methods. Furthermore, the results of the ablation study show that the knowledge database of ICD titles learned by the attention-based Bi-GRU enhances the feature expression and improves the prediction performance. [ABSTRACT FROM AUTHOR]

The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In electron microscope segmentation of ISBI challenge 2012, the concise architecture and outstanding performance of the U-shaped network are impressive. Then, a variety of segmentation models based on this architecture have been proposed for medical image segmentations. We present a comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies. The aim of this survey is twofold. First, we report the different extended U-shaped networks, discuss main state-of-the-art extended mechanisms, including residual mechanism, dense mechanism, dilated mechanism, attention mechanism, multi-module mechanism, and ensemble mechanism, analyze their pros and cons. Second, this survey provides the overview of studies in main application areas of U-shaped networks, including brain tumor, stroke, white matter hyperintensities (WMHs), eye, cardiac, liver, musculoskeletal, skin cancer, and neuronal pathology. Finally, we summarize the current U-shaped networks, point out the open challenges and directions for future research. [ABSTRACT FROM AUTHOR]

• We propose a deep convolutional neural network for improving the quality of the segmentation WMHs. • This network consists of two subnets which uses several architectural innovations to provide very accurate segmentation. • We also propose a novel loss function based on lesion similarity and background similarity. • Our network outperforms current segmentation methods on two public segmentation challenges. White matter hyperintensities (WMHs) appear as regions of abnormally signal intensity on magnetic resonance imaging (MRI) images, that can be identified in MRI images of elderly people and ischemic stroke patients. However, manual segmentation and quantification of images with WMHs is laborious and time-consuming. Moreover, ischemic stroke lesion and WMHs appear as similar signals in MRI images, making it difficult to accurately segment the WMHs. Analysis of WMH-containing images is important for clinical diagnosis, and thus several segmentation methods have been proposed. However, these methods cannot accurately differentiate WMH and ischemic stroke lesions. We propose a deep convolutional neural network, M2DCNN, that can accurately segment WMHs and distinguish them from ischemic stroke lesions. M2DCNN consists of two subnets that rely on a set of novel multi-scale features and a novel architecture (inclusion of dense and dilated blocks). Our model is trained and evaluated on two public segmentation challenges with multi-modality MRI images. Empirical tests demonstrate that M2DCNN outperforms current segmentation methods. We empirically demonstrate that M2DCNN effectively separates WMHs from stroke lesions. Finally, ablation experiments reveal that both multi-scale features and architectural elements in our method contribute to the improved predictive performance. [ABSTRACT FROM AUTHOR]

Nonnegative matrix factorization (NMF) is a very popular unsupervised or semi-supervised learning method useful in various applications including data clustering, image processing, and semantic analysis of documents. This study focuses on Symmetric NMF (SNMF), which is a special case of NMF and can be useful in network analysis. Although there exist several algorithms for SNMF in literature, their convergence and initialization have not been well addressed. In this paper, we first discuss the convergence and initialization of existing algorithms for SNMF. We then propose a Converged Algorithm for SNMF (called CASNMF) which minimizes the Euclidean distance between a symmetrical matrix and its approximation of SNMF. Based on the optimization principle and the local auxiliary function method, we prove that our presented CASNMF does not only converge to a stationary point, but also could be applied to the wider range of SNMF problems. In addition, CASNMF does not require that the initial values are nonzero. To verify our theoretical results, experiments on three data sets are conducted by comparing our proposed CASNMF with other existing methods. [ABSTRACT FROM AUTHOR]

Identifying interactions between drug compounds and target proteins is an important process in drug discovery. It is time-consuming and expensive to determine interactions between drug compounds and target proteins with experimental methods. The computational methods provide an effective strategy to address this issue. The difficulties of drug–target interaction identification include the lack of known drug–target association and no experimentally verified negative samples. In this work, we present a method, called PUDT, to predict drug–target interactions. Instead of treating unknown interactions as negative samples, we set it as unlabeled samples. We use three strategies (Random walk with restarts, KNN and heat kernel diffusion) to part unlabeled samples into two groups: reliable negative samples ( RN ) and likely negative samples ( LN ) based on target similarity information. Then, majority voting method is used to aggregate these strategies to decide the final label of unlabeled samples. Finally, weighted support vector machine is employed to build a classifier. Four datasets (enzyme, ion channel, GPCR and nuclear receptor) are used to evaluate the performance of our method. The results demonstrate that the performance of our method is comparable or better than recent state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

As genes with similar functions are closely related, cluster analysis becomes an important tool to understand and predicts gene functions (patterns) from gene expression profiles. In many situations, each gene expression profile only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profiles cannot obtain biological meaningful patterns. In this paper, we propose a novel method to discover biologically meaningful patterns by clustering short time-series gene expression profiles with integrating protein–protein interaction (PPI) data. Numerical experiments are conducted on two sets of Arabidopsis thaliana short time-series gene expression profiles, with treatments of cold stress and drought stress, respectively. The proposed method can effectively assign genes belonging to target functional clusters (patterns), in terms of having small p-value of GO term ‘response to cold’ ( GO:0009409 ) in dataset one, and small p-value of GO term ‘response to water deprivation’ ( GO:0009414 ) in dataset two than those from an existing clustering algorithm (namely STEM) for short time-series gene expression profiles. Additionally, our proposed method is able to identify higher percentage of stress-related genes and un-annotated genes in resultant cluster than STEM for both datasets; which does not only improve gene clustering effectiveness, but also contribute to functional prediction of un-annotated genes. [ABSTRACT FROM AUTHOR]