Abstract:Contrast-enhanced ultrasound （CEUS） is a powerful diagnostic tool that enhances blood flow signals from tumor micro-vessels through the peripheral venous injection of ultrasound contrast agents. This enables clinical physicians to dynamically evaluate tumor angiogenesis in real-time. CEUS imaging is widely used for the diagnosis， postoperative evaluation， and treatment planning of multiple organs. In recent years， deep learning techniques have made considerable progress， offering new opportunities for the intelligent analysis of dynamic CEUS. Deep learning methods have widened the scope of clinical applications largely， improving its efficacy of diagnosis and treatment. However， similar to the traditional ultrasound imaging， CEUS is faced with the challenges of interference from speckle noise， respiratory motion， and low standardization， making the analysis of spatial-temporal information of dynamic perfusion become difficult. This paper systematically reviews recent research on the intelligent analysis of CEUS， covering clinical applications such as benign-malignant differentiation， malignant grading， therapeutic prediction， and the selection of diagnosis and treatment plans. We summarize the latest advances of radiomic and deep learning methods in the area of CEUS imaging analysis， and highlight the limitations of current research and future directions for development.

Abstract:Sleep staging is a vital process for analyzing polysomnographic recordings， which plays a key role in sleep monitoring and diagnosis of sleep disorders. Traditional manual sleep staging requires expertise， which is cumbersome and time-consuming. Deep learning constructs models by simulating the mechanism of human brain to interpret information， and has powerful automatic feature extraction and feature expression functions. Applying deep learning method to the research of sleep staging does not rely on manually designed features and can realize the automation of sleep staging. This article emphasizes on some typical automatic sleep staging studies since 2017， and conducts a systematic review of deep learning model applied in automatic sleep staging from two aspects of single-view and multi-view input. Then， the difficulties of deep learning model based on multi-view input are analyzed and its potential research value is pointed out. Finally， possible future research direction is discussed.

Abstract:In recent years， deep learning methods have been widely applied to various disease prediction tasks， even surpassing human experts in some aspects. However， the black box nature of the algorithm limits its clinical application. In this paper， the knowledge representation and reasoning learning and deep learning methods are combined to build an interpretable deep learning model incorporating knowledge representation and reasoning vectors. The model first builds a relationship graph between physical examination indicators and test values according to the normal range of physical examination indicators， and the relationship graph between physical examination indicators and test values is coded through the deep learning model based on knowledge representation and reasoning learning. Then， the patients’ physical examination data are expressed as vectors， which are input into the self-attention mechanism and the classifier constructed by convolutional neural network to realize the disease prediction. When the model is applied to the prediction experiment of diabetes， the accuracy and recall of the model are better than those of the comparative machine learning methods. Compared with the random forest algorithm， the accuracy and recall are also improved by 0.81% and 5.21%， respectively. Experimental results show that the application of knowledge representation and reasoning learning and deep learning technological convergence to diabetes prediction through interpretable methods can achieve the purpose of early detection and auxiliary diagnosis of diabetes.

Abstract:Early mycosis fungoides （MFs） may present as erythematous scaly skin lesions， which are difficult to distinguish from benign inflammatory skin diseases such as psoriasis and chronic eczema. This paper presents a new method based on multimodal image fusion for early mycosis fungoides recognition. The method adopts the ResNet18 network to extract features of single-modality images based on dermoscopic images and clinical images， designs the cross-modal attention module to achieve feature fusion of two modal images， and uses the self-attention module to extract the key information and reduce redundant information in the fusion features， thereby improving the accuracy of intelligent identification of early mycosis fungoides. Experimental results show that the proposed intelligent diagnosis model outperforms the comparison algorithms. At the same time， the proposed intelligent model is applied to the actual clinical diagnosis of dermatologists. Through the changes in the average diagnostic accuracy of the experimental group and the control group， it is confirmed that the proposed intelligent diagnostic model can effectively improve the clinical diagnosis level.

Abstract:About a third of the world’s population suffers from insomnia， and many studies have shown that elevating high frequency band activity is an important cause of insomnia. However， due to the existence of large disturbance factors， it is difficult to evaluate in daily resting state conditions. Therefore， the Beta and Gamma bands of electroencephalogram （EEG） are extracted from patients with primary insomnia and normal controls. The phase locking value （PLV）， which is more suitable for nonlinear and non-stationary signals such as EEG， is used to obtain the adjacency matrix to construct rest-state functional brain network. The adaptive threshold technology is used to binarize the adjacency matrix. In order to fuse various characteristics of brain networks， a comprehensive measurement index of brain networks is proposed for insomnia detection. In Beta frequency band， the comprehensive indexes are significantly different between the primary insomnia group and the normal control group （p=0.044）. The automatic classification using support vector machine （SVM） achieves the accuracy of 77.7% and the sensitivity of 90.7% in Beta band. Compared with the original network characteristics， the classification accuracy and the sensitivity of the proposed comprehensive index are increased by 9.4% and 20.7%， respectively. At the same time， compared with the existing studies， the classification accuracy and the sensitivity of the proposed comprehensive index are increased by 19.4% and 20.7%， respectively. It shows the proposed method has potential application value in the diagnosis of insomnia.

Abstract:To accurately evaluate individual emotional states， we propose a graph feature learning and recognition algorithm for electroencephalogram（EEG）-based emotion recognition. In the proposed algorithm， the original EEG data are first used to construct the corresponding EEG network. And then， the local adjacency graph between different emotional EEG network samples is constructed in the high-dimensional EEG brain network space， which aims to capture the distribution of the emotional EEG brain networks， and the graph Laplacian matrix can be estimated with the adjacency graph. Thirdly， the optimal low-dimensional graph embeddings of emotional EEG brain networks are obtained by the spectral graph theory， and the emotional EEG brain network samples can be represented in the low-dimensional space， in which the initial emotional EEG brain networks can be represented with a set of network features. Finally， based on the extracted emotional EEG brain network features， the optimal support vector machine classifier is trained and utilized in the emotion recognition. The verification experiment is carried out on the international public emotional EEG datasets， and experimental results show that compared with traditional emotion recognition algorithms， the proposed method can effectively improve the accuracy of emotion recognition， and achieve a robust recognition effect of 91.85% （SEED dataset， 3-class）， 79.36% （MAHNOB-HCI dataset， 3-class） and 79% （DEAP dataset， 4-class） on three public datasets， respectively.

Abstract:Few-channel electroencephalogram （EEG） is more suitable and affordable for practical use as a portable or wearable device， but it is subject to a variety of unpredictable artifacts， making removal of artifacts extremely difficult. In the feature space， the artifact-related components are dispersed while the components related to brain activities are closely distributed. We propose an outlier detection-based method for artifact removal under the few-channel condition. The underlying components （sources） are extracted using wavelet decomposition and blind source separation methods， and the artifact-related components far from the center of distribution of all components are considered as outliers and are identified using one-class support vector machine. In the quantitative analyses with semi-simulated data， the proposed method outperforms the threshold-based methods for various artifacts， including EMG， electro-oculogram（EOG） and power line noise. The visualization of the clusters of components demonstrates the effectiveness of the hypothesis. This study innovatively combines the ideas of blind source separation and outlier detection， without setting artifact-specific parameters， and is capable of adaptively removing various artifacts while effectively retaining brain activities， showing excellent performance and usability.

Abstract:Because conventional morphological indicators such as volume and surface area are too general for the subcortical nuclei， it is difficult to detect the subtle changes in the surface morphology using traditional morphological feature acquisition methods. To solve this problem， we propose a fine feature extraction algorithm for subcortical nuclei and apply it to the cognitive state prediction task of the elderly. Using surface conformal parameterization， surface conformal representation， and the surface fluid registration based on mutual information， 15 000×2 morphological features are extracted from both the bilateral hippocampus and amygdala of 46 subjects. Using the dimensionality reduction process， including patch selection， sparse coding and dictionary learning， and max-pooling， we avoid the dimensionality curse while fully preserving the texture information of nuclei. Finally， taking tree as the weak learner， we integrate the final strong classifier using the GentleBoost algorithm for cognitive prediction. The results show that the prediction accuracy of 85% could be achieved only by the novel features of the hippocampus and amygdala， providing a new way perspective for fine feature mining of subcortical structures.

Abstract:Heart sound signal is an important signal for analyzing and diagnosing heart problems， and heart sound segmentation is an essential part before analyzing and processing it. By separating the heart sound segmentation task into two sub tasks of localization and recognition， this paper proposes a two-stage convolutional neural network， which is composed of localization network and discrimination network to complete the recognition and localization of heart sound signals respectively. First， the original signal is divided into frames through a sliding window， then the spectrum is obtained by short time Fourier transform， and then the Mel frequency spectral coefficient（MFSC） characteristics are obtained by Mel filter. The first localization model is input to judge whether it is a heart sound segment. If so， the discrimination neural network is input to identify the first heart sound and the second heart sound， so as to achieve heart sound segmentation. At last， multi frame voting results are used to reduce the misjudgment. At the same time， the spatial attention mechanism is introduced into the convolutional neural network. Experimental results show that this two-stage neural network model with attention mechanism has higher accuracy in heart sound segmentation tasks than a single convolutional neural network classification model， and also makes the model more simple and lightweight.

Abstract:An improved AdaBoost reinforcement learning algorithm is proposed for distinguishing the breath signals of healthy patients and liver cancer patients. First， the breath signals of volunteers， including healthy controls and liver cancer patients， are collected and their main features are extracted by Relief algorithm. Then， based on Stacking model， several groups of base classifiers are trained by traditional machine learning algorithms and some sub-classifiers are then constructed. To reduce the influence of training samples on the classifier performance， a K-fold crossover is applied， and k base classifiers could be successively obtained to form a base classifier group. Further， the prediction results of this base classifier group， i.e.， sub-classifiers on the test set， are obtained by the voting method. Then， according to the prediction error rate of each sub-classifier on the training set， the training set is updated and the weight coefficients of each sub-classifier are obtained according to the prediction error rate of each sub-classifier on the training set. Finally， the prediction results of multiple sub-classifiers are weighted and combined to obtain the final prediction results. Experimental results show that the improved AdaBoost algorithm can achieve an accuracy of about 90% and the specificity and precision are more than 95% in discriminating the breath of liver cancer from the breath of healthy controls. Compared with the traditional AdaBoost algorithm， the proposed algorithm has significantly lower error rate and improved robustness when used for liver cancer breath detection. Therefore， the improved AdaBoost algorithm can effectively improve the accuracy of liver cancer breath identification， which is important for the research of identifying liver cancer by breath for early diagnosis.

Abstract:In order to segment thyroid nodules more accurately， this paper proposes an improved fully convolutional network （FCN） segmentation model. Compared with FCN， the atrous spatial pyramid pooling （ASPP） module and the multi-layer feature transfer （FT） module are added. The decoder module in LinkNet model is used for up-sampling， and the VGG16 backbone network is used for feature extraction down-sampling. The experiment uses 17 413 ultrasound thyroid nodule images from Stanford AIMI shared data set for training， verification and testing， respectively. Experimental results show that compared with other segmentation models， the proposed model achieves 79.7%， 87.6% and 98.42% in mean intersection over union （mIoU）， Dice similarity coefficient and F₁ score respectively， achieving better segmentation effect and effectively improving the segmentation accuracy of thyroid nodules.

Abstract:The diagnosis of autism spectrum disorder （ASD） mainly relies on the patient’s medical history and clinical symptoms， and there is still a lack of objective evaluation indicators. Therefore， the discovery of disease-related biomarkers is essential for early identification and intervention. Although the multi-site brain imaging data have increased the sample size and improved the statistical power， which helps to improve the diagnostic performance of autism， the current research is often plagued by data heterogeneity. To address this issue， a discriminative domain adaption via low-rank representation （DDA-LRR） framework for multi-site ASD identification is proposed. Specifically， we first transfer both source and target data to a common subspace， where each source data can be represented by a combination of source samples such that the distribution differences can be well relieved. Then， we learn an orthogonal reconstruction matrix， which can preserve the main energy in the obtained low-dimensional embedding space and thus is appropriate for the subsequent learning tasks. Finally， to ensure the discriminative ability of the low-rank representation， we use the label information of the source data to integrate the classification loss into the training stage. An efficient optimization strategy based on the alternating direction method of multipliers method is developed to solve the proposed DDA-LRR method. Experimental results show that the proposed method can reduce the differences in data distributions of multiple sites， realize the effective transfer of knowledge， and improve the diagnosis performance of multi-site ASD effectively.

Abstract:For less efficiency and low accuracy of predicting on hypertensive target organ damage， this paper proposes a prediction of hypertensive pulse wave based on mel frequency ceptral coefficient （MFCC）-based feature maps to accomplish the efficient and non-invasive diagnosis on target organ damage. For low accuracy of pulse-taking classification in temporal domain， pulse wave is transformed to the MFCC-based feature maps in frequency domain via replacing angular filter with Gaussian filter， an improved EfficientNet model， EfficientNetS is employed to enhance the ability of global feature extraction via adding the improved SiMAM attention mechanism. The clinical 608 cases of hypertension target organ damage concerning pulse-taking diagnosis are used. The evaluation indicators of five-fold cross-validation classification， i.e. F₁ score， accuracy， precision， sensitivity， area under the curve （AUC）， are 97.31%， 98.72%， 97.71%， 97.04%， 99.13%， respectively. Compared to the typical models， the proposed method has higher classification accuracy and generalization performance. In addition， this paper also studies the correlation between classification of pulse wave and its features， and analyzes the feature importance ranking in temporal domain and frequency domain of pulse-taking， which can help clinicians seek the occurrence mechanisms of hypertension caused by target organ damage， and find the effective measurements for timely prevention and treatment.

Abstract:The clinical activity score （CAS） is one of the important assessment methods for clinical diagnosis of thyroid associated ophthalmopathy （TAO） disease. Manual diagnosis of TAO is susceptible to the subjective experience of ophthalmologists due to the diversity of TAO symptoms and the influence of non-diseased areas. The accurate acquisition of key facial areas of TAO patients is one of the significant prerequisites for early diagnosis of TAO. Therefore， this paper proposes a lightweight algorithm for automatic segmentation of TAO diseased areas based on DSE-Net. The DSE-Net adopts U-Net as the backbone model， and the dense squeeze-and-excitation （DSE） channel attention module， which is designed to extract low-level features of the encoding structure layer by layer and fuse high-level features of the decoding structure layer， further enhances the feature extraction capability of the model. Tests on the sclera， eyelid， and lacrimal caruncle datasets demonstrate the effectiveness of DSE-Net， with Dice coefficients reaching 84.8%， 84.7%， and 92.7%， and IoUs reaching 74.0%， 74.7%， and 86.5%， respectively. The superiority of DSE-Net is also proved by a large number of comparative experiments. The proposed model has fewer parameters， simple structure and strong feature extraction ability， providing significant information for the early diagnosis and prognosis treatment of TAO.

Abstract:In the non-contact heart rate detection method based on ballistocardiogram， the actual shape of ballistocardiogram signals is easily covered up during notable body movements. To address the obstruction caused by invalid signals in locating the heartbeat point， a waveform compensation model for notable movement segments is proposed， which combines phase space reconstruction with RBF neural network. Firstly， the improved C-C method is used to select the appropriate reconstruction parameters. Then， the network topology is determined by dynamic k-means clustering. Transform the time series before the movement into phase points in reconstructed space， and feed them into the model as learning samples. Finally， the single-step recursive prediction of invalid signal segment is realized. Experimental results show that the prediction model has good accuracy and it can reduce the influence of irregular noise in the original signal. After model modification， the mean error of beat by beat cardiac cycle is 1.27% and the mean absolute error is 8.9 ms， effectively avoiding the misjudgment of heartbeat events.

Abstract:The shape， color and texture of skin lesions are very different， and the boundaries are not clear， which makes it difficult for the traditional deep learning methods to segment them accurately. Based on the above challenge， this paper proposes a residual Inception and bidirectional convolutional gated recurrent unit （ConvGRU） empowered intelligent segmentation model for skin lesion. Specifically， a cloud-edge collaboration intelligent segmentation service network model for skin lesion is firstly designed. By this network model， users can obtain quick and accurate segmentation services. Furthermore， a novel intelligent segmentation model for skin lesion is developed. By integrating residual Inception and bidirectional ConvGRU， this model can fuse multi-scale features and make full use of the relationship between low-level features and semantic features. It improves the ability of the model to extract features and capture global context information， and leads to better segmentation performance. Finally， experimental results on ISIC 2018 dataset show that the proposed intelligent segmentation model achieves higher accuracy and Jaccard coefficient than several recently proposed U-Net extended models.

Abstract:Unsupervised domain adaptation （UDA） methods leverage global feature distribution matching to realize knowledge transfer from source domain to target domain， while ignoring fine-grained local instance information. An unsupervised person re-identification method based on two-tiered domain adaptation （TTDA） is proposed， in which the omni-scale network（OSNet） is selected as the backbone network， and global feature distribution matching and localized instance matching between source and target domains are performed jointly in an end-to-end deep learning framework. And in order to effectively mine transferable useful knowledge from associations of different pedestrian IDs between source and target domains， the cross-domain adaptability is improved with a knowledge selection mechanism. Experimental results on multiple large-scale public datasets show that compared with other state-of-the-art methods， the proposed method achieves significant improvements in terms of mean average precision （mAP） and top-k hit rate for unsupervised cross-domain person re-identification tasks.

Abstract:Images acquired in low-light environments always suffer from low brightness， color distortion， loss of detail information， low contrast， and other problems. To meet the needs of subjective visual experience， researchers often enhance the images. However， the impact of image enhancement on the performance of machine vision applications is not systematically researched. In this paper， we first summarize typical low-light image enhancement methods and semantic segmentation methods. Next， we take a machine vision application （i.e.， semantic segmentation） as an example and select the low-light scene to investigate the effect of image enhancement methods on the semantic segmentation performance of the low-light scene. The experimental results show that enhancement processing can improve the visual effect of images， but may introduce noise. In addition， image enhancement methods and semantic segmentation methods do not concentrate exactly on the same focus and features. Therefore， image enhancement doesnot contribute significantly to the performance of semantic segmentation in low-light scenes， and even brings negative effects.

Abstract:Due to the effect of image decorrelation under large deformation fields， digital image correlation（DIC） has never been able to complete parallel computation between images. In order to break through this bottleneck， this paper proposes an accelerated-KAZE（AKAZE）-based reference image update method， which can complete the reference image update before DIC is officially calculated， and provide independent data for parallel computing. A graphics processing unit（GPU） parallel computing architecture is constructed， which can independently estimate all subsizes and complete the parallel computation between images and subsizes. Finally， tensile tests are performed on the nitrile butadiene rubber（NBR）， and the results show that compared with the traditional serial DIC calculation method， the proposed parallel method can be increased by two orders of magnitude.

Abstract:With the rapid development of wireless communication technology， the number of wireless access devices is increasing while the energy consumption of the system is also increasing. An orthogonal frequency division multiplexing（OFDM） system with wireless energy-carrying communication capability can effectively improve energy efficiency. Aiming at the problem of resource allocation with system energy efficiency as the optimization goal， an energy efficiency optimization algorithm for energy-carrying communication OFDM systems based on ellipsoid method is proposed. The algorithm uses ellipsoid method to update the Lagrange multiplier， which can effectively accelerate the convergence speed and improve the performance of the algorithm. Simulation results show that the proposed algorithm can effectively solve the resource allocation problem with system energy efficiency as the optimization objective. Compared with the subgradient method， the ellipsoid method has a faster convergence speed and can significantly reduce the complexity of the algorithm.