Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis

BackgroundFrequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques.MethodIn this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification.ResultsAn experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods.ConclusionAchieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.     

基于卷积神经网络的无人机遥感影像水鸟自动识别

水鸟监测是了解水鸟种群和分布动态、开展水鸟和湿地保护的基础,但该活动耗时耗力。近年来,随着无人机遥感技术的发展,使用小型无人机获得高分辨率的水鸟遥感影像已经成为可能;与此同时,卷积神经网络提供了一种快速识别无人机遥感图像中的鸟类的方法。我们尝试结合两种技术,使用卷积神经网络Mask R-CNN与YOLOv3识别湖南西洞庭湖国家级自然保护区无人机遥感影像中的大型水鸟,取得了良好的效果：模型检测拍摄到的鸭属鸟类,包括绿翅鸭(Anas crecca)和罗纹鸭(A. falcata)的结果平均精度达到0.93,精度达到90.83%,召回率达到93%;检测小天鹅(Cygnus columbianus)的结果平均精度达到0.91,精度达到84.38%,召回率达到84.00%。结果表明,将无人机遥感技术与卷积神经网络结合,可以快速统计水鸟数量,在种群监测工作中具有应用潜力。     

Bilinear neural network with 3-D attention for brain decoding of motor imagery movements from the human EEG

Deep learning has achieved great success in areas such as computer vision and natural language processing. In the past, some work used convolutional networks to process EEG signals and reached or exceeded traditional machine learning methods. We propose a novel network structure and call it QNet. It contains a newly designed attention module: 3D-AM, which is used to learn the attention weights of EEG channels, time points, and feature maps. It provides a way to automatically learn the electrode and time selection. QNet uses a dual branch structure to fuse bilinear vectors for classification. It performs four, three, and two classes on the EEG Motor Movement/Imagery Dataset. The average cross-validation accuracy of 65.82%, 74.75%, and 82.88% was obtained, which are 7.24%, 4.93%, and 2.45% outperforms than the state-of-the-art, respectively. The article also visualizes the attention weights learned by QNet and shows its possible application for electrode channel selection.     

A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography

BackgroundDiabetic retinopathy (DR) is one of the major causes of blindness in adults suffering from diabetes. With the development of wide-field optical coherence tomography angiography (WF-OCTA), it is to become a gold standard for diagnosing DR. The demand for automated DR diagnosis system based on OCTA images have been fostered due to large diabetic population and pervasiveness of retinopathy cases.Materials and methodsIn this study, 288 diabetic patients and 97 healthy people were imaged by the swept-source optical coherence tomography (SS-OCT) with 12 mm × 12 mm single scan centered on the fovea. A multi-branch convolutional neural network (CNN) was proposed to classify WF-OCTA images into four grades: no DR, mild non-proliferative diabetic retinopathy (NPDR), moderate to severe NPDR, and proliferative diabetic retinopathy (PDR).ResultsThe proposed model achieved a classification accuracy of 96.11%, sensitivity of 98.08% and specificity of 89.43% in detecting DR. The accuracy of the model for DR staging is 90.56%, which is higher than that of other mainstream convolution neural network models.ConclusionThis technology enables early diagnosis and objective tracking of disease progression, which may be useful for optimal treatment to reduce vision loss.     

Classification of Leukemia and Leukemoid Using VGG-16 Convolutional Neural Network Architecture

Leukemoid reaction like leukemia indicates noticeable increased count of WBCs (White Blood Cells) but the cause of it is due to severe inflammation or infections in other body regions. In automatic diagnosis in classifying leukemia and leukemoid reactions, ALL IDB2 (Acute Lymphoblastic Leukemia-Image Data Base) dataset has been used which comprises 110 training images of blast cells and healthy cells. This paper aimed at an automatic process to distinguish leukemia and leukemoid reactions from blood smear images using Machine Learning. Initially, automatic detection and counting of WBC is done to identify leukocytosis and then an automatic detection of WBC blasts is performed to support classification of leukemia and leukemoid reactions. Leukocytosis is commonly observed both in leukemia and leukemoid hence physicians may have chance of wrong diagnosis of malignant leukemia for the patients with leukemoid reactions. BCCD (blood cell count detection) Dataset has been used which has 364 blood smear images of which 349 are of single WBC type. The Image segmentation algorithm of Hue Saturation Value color based on watershed has been applied. VGG16 (Visual Geometric Group) CNN (Convolution Neural Network) architecture based deep learning technique is being incorporated for classification and counting WBC type from segmented images. The VGG16 architecture based CNN used for classification and segmented images obtained from first part were tested to identify WBC blasts.     

Research on Plant Species Identification Based on Improved Convolutional Neural Network

Plant species recognition is an important research area in image recognition in recent years. However, the existing plant species recognition methods have low recognition accuracy and do not meet professional requirements in terms of recognition accuracy. Therefore, ShuffleNetV2 was improved by combining the current hot concern mechanism, convolution kernel size adjustment, convolution tailoring, and CSP technology to improve the accuracy and reduce the amount of computation in this study. Six convolutional neural network models with sufficient trainable parameters were designed for differentiation learning. The SGD algorithm is used to optimize the training process to avoid overfitting or falling into the local optimum. In this paper, a conventional plant image dataset TJAU10 collected by cell phones in a natural context was constructed, containing 3000 images of 10 plant species on the campus of Tianjin Agricultural University. Finally, the improved model is compared with the baseline version of the model, which achieves better results in terms of improving accuracy and reducing the computational effort. The recognition accuracy tested on the TJAU10 dataset reaches up to 98.3%, and the recognition precision reaches up to 93.6%, which is 5.1% better than the original model and reduces the computational effort by about 31% compared with the original model. In addition, the experimental results were evaluated using metrics such as the confusion matrix, which can meet the requirements of professionals for the accurate identification of plant species.     

Online Classification of Multiple Motor Imagery Tasks Using Filter Bank Based Maximum-a-Posteriori Common Spatial Pattern Filters

ObjectiveThe main objective of this paper is to propose a novel technique, called filter bank maximum a-posteriori common spatial pattern (FB-MAP-CSP) algorithm, for online classification of multiple motor imagery activities using electroencephalography (EEG) signals. The proposed technique addresses the overfitting issue of CSP in addition to utilizing the spectral information of EEG signals inside the framework of filter banks while extending it to more than two conditions.Materials and methodsThe classification of motor imagery signals is based upon the detection of event-related de-synchronization (ERD) phenomena in the μ and β rhythms of EEG signals. Accordingly, two modifications in the existing MAP-CSP technique are presented: (i) The (pre-processed) EEG signals are spectrally filtered by a bank of filters lying in the μ and β brainwave frequency range, (ii) the framework of MAP-CSP is extended to deal with multiple (more than two) motor imagery tasks classification and the spatial filters thus obtained are calculated for each sub-band, separately. Subsequently, the most imperative features over all sub-bands are selected and un-regularized linear discriminant analysis is employed for classification of multiple motor imagery tasks.ResultsPublicly available dataset (BCI Competition IV Dataset I) is used to validate the proposed method i.e. FB-MAP-CSP. The results show that the proposed method yields superior classification results, in addition to be computationally more efficient in the case of online implementation, as compared to the conventional CSP based techniques and its variants for multiclass motor imagery classification.ConclusionThe proposed FB-MAP-CSP algorithm is found to be a potential / superior method for classifying multi-condition motor imagery EEG signals in comparison to FBCSP based techniques.     

基于点云卷积神经网络的蛋白质柔性预测

目的 蛋白质的柔性运动对生物体各种反应有着重要意义,基于蛋白质的空间结构预测其柔性运动是蛋白质结构-功能关系领域的重要问题.卷积神经网络(convolutional neural network,CNN)在蛋白质结构-功能关系研究中已有成功应用.方法 本研究借鉴计算机视觉研究中PointNet方法的思想,提出了一种蛋白...     

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network

Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and development. With the rapid advancement of deep learning, computational methods are increasingly applied to screen drug-target interactions. Many methods consider this problem as a binary classification task (binding or not), but ignore the quantitative binding affinity. In this paper, we propose a new end-to-end deep learning method called DeepMHADTA, which uses the multi-head self-attention mechanism in a deep residual network to predict drug-target binding affinity. On two benchmark datasets, our method outperformed several current state-of-the-art methods in terms of multiple performance measures, including mean square error (MSE), consistency index (CI), rm2, and PR curve area (AUPR). The results demonstrated that our method achieved better performance in predicting the drug–target binding affinity.     

基于BP神经网络的siRNA活性预测

RNA干扰(RNAinterference,RNAi)是由双链RNA(dsRNA)引起的基因沉默现象,它通过降解具有同源序列的mRNA来起作用,特殊设计的siRNA能使靶基因发生特异性沉默,起到确定基因功能或沉默致病基因从而治疗疾病的目的。在RNAi技术的应用中,通常采用的是长度为19bp,正、反义链3'端各有2个不配对碱基的双链RNA(siRNA)。但针对靶基因不同位点设计的siRNA作用效果差别很大。影响siRNA效果的因素是多方面的,这些因素的作用又是非线性的。本文在研究影响siRNA作用效果的各种因素的基础上,对已经公开发表的实验数据进行特征提取,作为BP神经网络的训练数据,并将训练好的BP神经网络用于siRNA活性预测。     

SegNet and Salp Water Optimization-driven Deep Belief Network for Segmentation and Classification of Brain Tumor

Classification of brain tumor in Magnetic Resonance Imaging (MRI) images is highly popular in treatment planning, early diagnosis, and outcome evaluation. It is very difficult for classifying and diagnosing tumors from several images. Thus, an automatic prediction strategy is essential in classifying brain tumors as malignant, core, edema, or benign. In this research, a novel approach using Salp Water Optimization-based Deep Belief network (SWO-based DBN) is introduced to classify brain tumor. At the initial stage, the input image is pre-processed to eradicate the artifacts present in input image. Following pre-processing, the segmentation is executed by SegNet, where the SegNet is trained using the proposed SWO. Moreover, the Convolutional Neural Network (CNN) features are employed to mine the features for future processing. At last, the introduced SWO-based DBN technique efficiently categorizes the brain tumor with respect to the extracted features. Thereafter, the produced output of the introduced SegNet + SWO-based DBN is made use of in brain tumor segmentation and classification. The developed technique produced better results with highest values of accuracy at 0.933, specificity at 0.880, and sensitivity at 0.938 using BRATS, 2018 datasets and accuracy at 0.921, specificity at 0.853, and sensitivity at 0.928 for BRATS, 2020 dataset.     

基于卷积神经网络和循环神经网络的环形RNA剪接位点识别研究

本文提出了一种基于卷积神经网络和循环神经网络的深度学习模型,通过分析基因组序列数据,识别人基因组中环形RNA剪接位点.首先,根据预处理后的核苷酸序列,设计了2种网络深度、8种卷积核大小和3种长短期记忆(long short term memory,LSTM)参数,共8组16个模型;其次,进一步针对池化层进行均值池化和最大池化的测试,并加入GC含量提高模型的预测能力;最后,对已经实验验证过的人类精浆中环形RNA进行了预测.结果表明,卷积核尺寸为32×4、深度为1、LSTM参数为32的模型识别率最高,在训练集上为0.9824,在测试数据集上准确率为0.95,并且在实验验证数据上的正确识别率为83%.该模型在人的环形RNA剪接位点识别方面具有较好的性能.     

脑电信号数据压缩及棘波识别的小波神经网络方法

本文在对小波神经网络及其算法研究的基础上,提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。实验结果显示,小波网络在大量压缩数据的同时,能够较好的恢复原有信号,另外,在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,说明此方法在电生理信号处理和时频分析方面有着光明的应用前景。     

基于人工神经网络的昆虫鸣声识别

以常见的7种飞虱雄虫求偶鸣声信号的频率峰值作为输入向量,用人工神经网络来识别它们的鸣声,平均识别率达90．6％。人工神经网络可以用于昆虫鸣声识别。     

A Review on Convolutional Neural Networks for Brain Tumor Segmentation: Methods,Datasets, Libraries,and Future Directions

ObjectivesAccurate and reliable segmentation of brain tumors from MRI images helps in planning an enhanced treatment and increases the life expectancy of patients. However, the manual segmentation of brain tumors is subjective and more prone to errors. Nonetheless, the recent advances in convolutional neural network (CNN)-based methods have exhibited outstanding potential in robust segmentation of brain tumors. This article comprehensively investigates recent advances in CNN-based methods for automatic segmentation of brain tumors from MRI images. It examines popular deep learning (DL) libraries/tools for an expeditious and effortless implementation of CNN models. Furthermore, a critical assessment of current DL architectures is delineated along with the scope of improvement.MethodsIn this work, more than 50 scientific papers from 2014-2020 are selected using Google Scholar and PubMed. Also, the leading journals related to our work along with proceedings from major conferences such as MICCAI, MIUA and ECCV are retrieved. This research investigated various annual challenges too related to this work including Multimodal Brain Tumor Segmentation Challenge (MICCAI BRATS) and Ischemic Stroke Lesion Segmentation Challenge (ISLES).ResultAfter a systematic literature search pertinent to the theme, we found that principally there exist three variations of CNN architecture for brain tumor segmentation: single-path and multi-path, fully convolutional, and cascaded CNNs. The respective performances of most automated methods based on CNN are appraised on the BraTS dataset, provided as a part of the MICCAI Multimodal Brain Tumor Segmentation challenge held annually since 2012.ConclusionNotwithstanding the remarkable potential of CNN-based methods, reliable and robust segmentation of brain tumors continues to be an intractable challenge. This is due to the intricate anatomy of the brain, variability in its appearance, and imperfection in image acquisition. Moreover, owing to the small size of MRI datasets, CNN-based methods cannot operate with their full capacity, as demonstrated with large scale datasets, such as ImageNet.     

基于卷积神经网络的超声影像肝癌自动分类

目的 针对从原发性肝癌中检测肝细胞癌（HCC）的灵敏度不高和诊断结果高度依赖放射科医生的专业性和临床经验，本文利用深度卷积神经网络（CNN）的方法自动学习B超和超声造影（CEUS）图像中的特征信息，并实现对肝癌的分类。方法 建立并验证基于CNN的多个二维（2D）和三维（3D）分类模型，分别对116例患者（其中100例HCC和16例非HCC）的B超和CEUS影像进行定量分析，并对比分析各个模型的分类性能。结果 实验结果表明，3D-CNN模型的各方面性能指标都优于2D-CNN模型，验证了3D-CNN模型能同时提取肿瘤区域的2D影像特征及血流时间动态变化特征，比2D-CNN模型更适用于HCC与非HCC分类。其中3D-CNN模型的AUC、准确率和敏感度值最高，分别达到了85%、85%和80%。此外，由于HCC和非HCC样本不均衡，通过扩充非HCC样本的数量可以提升网络的分类性能。结论 本文提出的3D-CNN模型能够实现快速、准确的肝癌分类，有望应用于辅助临床医师诊断与治疗肝癌。     

Interpretable Classification of Tauopathies with a Convolutional Neural Network Pipeline Using Transfer Learning and Validation against Post-Mortem Clinical Cases of Alzheimer’s Disease and Progressive Supranuclear Palsy

Neurodegenerative diseases, tauopathies, constitute a serious global health problem. The etiology of these diseases is unclear and an increase in their incidence has been projected in the next 30 years. Therefore, the study of the molecular mechanisms that might stop these neurodegenerative processes is very relevant. Classification of neurodegenerative diseases using Machine and Deep Learning algorithms has been widely studied for medical imaging such as Magnetic Resonance Imaging. However, post-mortem immunofluorescence imaging studies of the brains of patients have not yet been used for this purpose. These studies may represent a valuable tool for monitoring aberrant chemical changes or pathological post-translational modifications of the Tau polypeptide. We propose a Convolutional Neural Network pipeline for the classification of Tau pathology of Alzheimer’s disease and Progressive Supranuclear Palsy by analyzing post-mortem immunofluorescence images with different Tau biomarkers performed with models generated with the architecture ResNet-IFT using Transfer Learning. These models’ outputs were interpreted with interpretability algorithms such as Guided Grad-CAM and Occlusion Analysis. To determine the best classifier, four different architectures were tested. We demonstrated that our design was able to classify diseases with an accuracy of 98.41% on average whilst providing an interpretation concerning the proper classification involving different structural patterns in the immunoreactivity of the Tau protein in NFTs present in the brains of patients with Progressive Supranuclear Palsy and Alzheimer’s disease.     

Learning-Induced Synchronization and Plasticity of a Developing Neural Network

Learning-induced synchronization of a neural network at various developing stages is studied by computer simulations using a pulse-coupled neural network model in which the neuronal activity is simulated by a one-dimensional map. Two types of Hebbian plasticity rules are investigated and their differences are compared. For both models, our simulations show a logarithmic increase in the synchronous firing frequency of the network with the culturing time of the neural network. This result is consistent with recent experimental observations. To investigate how to control the synchronization behavior of a neural network after learning, we compare the occurrence of synchronization for four networks with different designed patterns under the influence of an external signal. The effect of such a signal on the network activity highly depends on the number of connections between neurons. We discuss the synaptic plasticity and enhancement effects for a random network after learning at various developing stages.     

Image Recognition of Breast Tumor Proliferation Level Based on Convolution Neural Network

Pathological slide is increasingly applied in the diagnosis of breast tumors despite the issues of large amount of data, slow viewing and high subjectivity. To overcome these problems, a micrograph recognition method based on convolutional neural network is proposed for pathological slide of breast tumor. Combined with multi-channel threshold and watershed segmentation, a sample database including single cell, adhesive cell and invalid cell was established. Then, the convolution neural network with six layers is constructed, which has ability to classify the stained breast tumor cells with accuracy of more than 90%, and evaluate the proliferation level with relative error of less than 5%. The experimental result indicates the effectiveness of this approach, and is useful for providing an objective basis for evaluating the malignancy of breast tumors.