Pervasive Attentive Neural Network for Intelligent Image Classification Based on N-CDE’s

The utilization of visual attention enhances the performance of image classification tasks.Previous attentionbased models have demonstrated notable performance,but many of these models exhibit reduced accuracy when confronted with inter-class and intra-class similarities and differences.Neural-Controlled Differential Equations(N-CDE’s)and Neural Ordinary Differential Equations(NODE’s)are extensively utilized within this context.NCDE’s possesses the capacity to effectively illustrate both inter-class and intra-class similarities and differences with enhanced clarity.To this end,an attentive neural network has been proposed to generate attention maps,which uses two different types of N-CDE’s,one for adopting hidden layers and the other to generate attention values.Two distinct attention techniques are implemented including time-wise attention,also referred to as bottom N-CDE’s;and element-wise attention,called topN-CDE’s.Additionally,a trainingmethodology is proposed to guarantee that the training problem is sufficiently presented.Two classification tasks including fine-grained visual classification andmulti-label classification,are utilized to evaluate the proposedmodel.The proposedmethodology is employed on five publicly available datasets,including CUB-200-2011,ImageNet-1K,PASCAL VOC 2007,PASCAL VOC 2012,and MS COCO.The obtained visualizations have demonstrated that N-CDE’s are better appropriate for attention-based activities in comparison to conventional NODE’s.

A Spectral Convolutional Neural Network Model Based on Adaptive Fick’s Law for Hyperspectral Image Classification

Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.

Phylogeny,character evolution,and classification of Selaginellaceae(lycophytes)

Selaginella is the largest and most taxonomically complex genus in lycophytes.The fact that over 750 species are currently treated in a single genus makes Selaginellales/Selaginellaceae unique in pteridophytes.Here we assembled a dataset of six existing and newly sampled plastid and nuclear loci with a total of 684 accessions(74%increase of the earlier largest sampling)representing ca.300 species to infer a new phylogeny.The evolution of 10 morphological characters is studied in the new phylogenetic context.Our major results include:(1)the nuclear and plastid phylogenies are congruent with each other and combined analysis well resolved and strongly supported the relationships of all but two major clades;(2)the Sinensis group is resolved as sister to S.subg.Pulviniella with strong support in two of the three analyses;(3)most morphological characters are highly homoplasious but some characters alone or combinations of characters well define the major clades in the family;and(4)an infrafamilial classification of Selaginellaceae is proposed and the currently defined Selaginella s.l.is split into seven subfamilies(corresponding to the current six subgenera t the Sinensis group)and 19 genera(the major diagnosable clades)with nine new species-poor genera.We support the conservation of Selaginella with a new type,S.flabellata,to minimize nomenclatural instability.We provide a key to subfamilies and genera,images illustrating their morphology,their morphological and geographical synopses,a list of constituent species,and necessary new combinations.This new classification will hopefully facilitate communication,promote further studies,and help conservation.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

Cesarean Sections according to the Robson’s Classification in Two University Hospitals of Yaoundé: Indications and Maternofetal Outcome

Introduction: Cesarean section is a surgical intervention which consists in the extraction of a fetus from the uterus after its incision. The rate of cesarean section varies depending on the country and the health facility. For this reason, in 2015, the World Health Organization (WHO) recommended the use of Robson’s classification to evaluate the practice of cesarean sections in order to identify the groups of women who had abnormally high rates. The objective of our study was to evaluate cesarean sections using the Robson’s classification in CHRACERH and in the Yaoundé Central Hospital (YCH). Methodology: We carried out a retrospective cross sectional and descriptive study in two (02) university hospitals in Yaoundé which took place from December 2017 to May 2018. We included in our study all women who gave birth over a period of two (02) years from January 2016 to December 2017 in these two health facilities. Our sampling was exhaustive over the study period. The parturients’ information was collected using an anonymous and pretested questionnaire. The Robson’s group of every parturient was determined. Descriptive parameters like mean and proportions were calculated. We compared the rates and indications of cesarean sections between the both hospitals using Chi2 test. Results: Out of 330 deliveries realized in CHRACERH, we had 90 cesarean sections;hence, a rate of 27.2%. Out of 1863 deliveries carried out at the YCH, 462 were by cesarean section, hence a rate of 24.8%. The women who belonged to groups 1, 3 and 5 contributed to the highest rates of cesarean sections in both hospitals: in CHRACERH, group 5 (31.1%), group 3 (20%) and group 1 (15.6%), at YCH: group 3 (22.5%), group 1 (21.6%) and group 5 (17.3%). The indications of the cesarean sections varied depending on the Robson’s group and the hospital, the principal indication in group 1 was acute fetal distress (28.6%) in CHRACERH and cephalopelvic disproportion (36.7%) at YCH. Cephalopelvic disproportion was the predominant indication in groups 3 of CHRACERH (44.4%) and YCH (39.2%). In groups 5, CHRACERH and of YCH, a scarred uterus was the principal indication for the cesarean section at 82.4% and 78.4% respectively. At CHRACERH, the maternofetal complications were more frequent in groups 1 and 2 at the YCH, this was the case mostly in groups 1 and 3. Conclusion: The Robson’s classification is an adequate tool for the evaluation and comparison of the rates of cesarean sections. The rates of cesarean section in CHRACERH (27.2%) and at YCH (24.8%) were higher than the rates recommended by WHO. Robson’s groups 1, 3 and 5 were identified as the groups most at risk for cesarean sections in the both hospitals.

ADASYN与类别逆比例加权法在阿尔茨海默病不平衡数据中的应用

目的利用自适应合成抽样(adaptive synthetic sampling,ADASYN)与类别逆比例加权法处理类别不平衡数据,结合分类器构建模型对阿尔茨海默病(alzheimer′s disease,AD)患者疾病进程进行分类预测。方法数据源自阿尔茨海默病神经影像学计划(Alzheimer′s disease neuroimaging initiative,ADNI),经随机森林填补缺失值,弹性网络筛选特征子集后,利用ADASYN与类别逆比例加权法处理类别不平衡数据。分别结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)构建四种模型:ADASYN-RF、ADASYN-SVM、加权随机森林(weighted random forest,WRF)、加权支持向量机(weighted support vector machine,WSVM),与RF、SVM比较分类性能。模型评价指标为宏观平均精确率(macro-average of precision,macro-P)、宏观平均召回率(macro-average of recall,macro-R)、宏观平均F1值(macro-average of F1-score,macro-F1)、准确率(accuracy,ACC)、Kappa值和AUC(area under the ROC curve)。结果ADASYN-RF的分类性能最优(Kappa值为0.938,AUC为0.980),ADASYN-SVM次之。利用ADASYN-RF预测得到的重要分类特征分别为CDRSB、LDELTOTAL、MMSE,在临床上均可得到证实。结论ADASYN与类别逆比例加权法都能辅助提升分类器性能,但ADASYN算法更优。

Diagnostic classification of endosonography for differentiating colorectal ulcerative diseases: A new statistical method

AIM To establish a classification method for differential diagnosis of colorectal ulcerative diseases, especially Crohn's disease(CD), primary intestinal lymphoma(PIL) and intestinal tuberculosis(ITB).METHODS We searched the in-patient medical record database for confirmed cases of CD, PIL and ITB from 2008 to 2015 at our center, collected data on endoscopic ultrasound(EUS) from randomly-chosen patients who formed the training set, conducted univariate logistic regression analysis to summarize EUS features of CD, PIL and ITB, and created a diagnostic classification method. All cases found to have colorectal ulcers using EUS were obtained from the endoscopy database and formed the test set. We then removed the cases which were easily diagnosed, and the remaining cases formed the perplexing test set. We re-diagnosed the cases in the three sets using the classification method, determined EUS diagnostic accuracies, and adjusted the classification accordingly. Finally, the re-diagnosing and accuracy-calculating steps were repeated.RESULTS In total, 272 CD, 60 PIL and 39 ITB cases were diagnosed from 2008 to 2015 based on the in-patient database, and 200 CD, 30 PIL and 20 ITB cases were randomly chosen to form the training set. The EUS features were summarized as follows: CD: Thickened submucosa with a slightly high echo level and visible layer; PIL: Absent layer and diffuse hypoechoic mass; and ITB: Thickened mucosa with a high or slightly high echo level and visible layer. The test set consisted of 77 CD, 30 PIL, 23 ITB and 140 cases of other diseases obtained from the endoscopy database. Seventy-four cases were excluded to form the perplexing test set. After adjustment of the classification, EUS diagnostic accuracies for CD, PIL and ITB were 83.6%(209/250), 97.2%(243/250) and 85.6%(214/250) in the training set, were 89.3%(241/270), 97.8%(264/270) and 84.1%(227/270) in the test set, and were 86.7%(170/196), 98.0%(192/196) and 85.2%(167/196) in the perplexing set, respectively.CONCLUSION The EUS features of CD, PIL and ITB are different. The diagnostic classification method is reliable in the differential diagnosis of colorectal ulcerative diseases.

EVALUATION OF INTERNATIONAL CLASSIFICATION CRITERIA (2002) FOR PRIMARY SJGREN'S SYNDROME IN CHINESE PATIENTS

Objective To evaluate the sensitivity and specificity of international classification criteria （2002） for primary Sjogren＇s syndrome （pSS） and the role of lower lip biopsy in diagnosis of pSS in Chinese patients. Mothoda Patients who were diagnosed by the experts/rheumatologists as pSS during 1990-2002 from the Department of Rheumatology, Peking Union Medical College Hospital were retrospectively collected as experimental group. Patients who were diagnosed as non-pSS connective tissue diseases or non-connective tissue diseases served as control group. Those with a history of head-neck radiation, hepatitis C virus infection, AIDS, lymphoma, sarcoidosis, graft versus host disease （GVHD）, and anti-acetylcholine drug use were exempted. Both groups were required to complete questionnaires about symptoms such as dry eyes and dry mouth, and complete the objective tests of keratoconjunctivitis and xerostomia including Schirmer test, corneal staining, unstimulated salivary flow, sialography, lower lip biopsy, and antinuclear antibodies （including anti-SSA/SSB antibodies） test. Results A total of 330 pSS patients were included in experimental group and 185 non-pSS patients in control group. The mean age of both groups matched （47.8 ± 10.9 years vs. 46.2±13.6 years, P 〉 0.05）. The sensitivities of the criteria in pSS patients with lower lip biopsy and in pSS patients without lower lip biopsy were 89.2% and 87.2%, respectively; the overall sensitivity was 88.5%. The specificity was 97.3%. A total of 11.3% pSS patients with negative anti-SSA/SSB antibodies were diagnosed as pSS by lower lip biopsy. Coadwion The international classification criteria （2002） for pSS is feasible in Chinese patients. It has high sensitivity and specificity, and may serve as diagnosis criteria in routine clinical practice.

Performance of the Montreal classification for inflammatory bowel diseases

AIM: To validate the Montreal classification system for Crohn’s disease (CD) and ulcerative colitis (UC) within the Netherlands.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

Classification and 3-D distribution of upper layer water masses in the northern South China Sea

Using the fuzzy cluster analysis and the temperature-salinity(T-S) similarity number analysis of cruise conductivity-temperature-depth(CTD) data in the upper layer(0–300 m) of the northern South China Sea(NSCS), we classify the upper layer water of the NSCS into six water masses: diluted water(D), surface water(SS),the SCS subsurface water mass(U_S), the Pacific Ocean subsurface water mass(U_P), surface-subsurface mixed water(SU) and subsurface-intermediate mixed water(UI). A new stacked stereogram is used to illustrate the water mass distribution, and to examine the source and the distribution of U_P, combining with the sea surface height data and geostrophic current field. The results show that water mass U_P exists in all four seasons with the maximum range in spring and the minimum range in summer. In spring and winter, the U_P intrudes into the Luzon Strait and the southwest of Taiwan Island via the northern Luzon Strait in the form of nonlinear Rossby eddies, and forms a high temperature and high salinity zone east of the Dongsha Islands. In summer, the U_P is sporadically distributed in the study area. In autumn, the U_P is located in the upper 200 m layer east of Hainan Island.

Perianal Crohn’s disease:Still more questions than answers

In this editorial we comment on the article by Pacheco et al published in a recent issue of the World Journal of Gastroenterology.We focus specifically on the burden of illness associated with perianal fistulizing Crohn’s disease(PFCD)and the diagnostic and therapeutic challenges in the management of this condition.Evol-ving evidence has shifted the diagnostic framework for PFCD from anatomical classification systems,to one that is more nuanced and patient-focused to drive ongoing decision making.This editorial aims to reflect on these aspects to help clinicians face the challenge of PFCD in day-to-day clinical practice.

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and AD.However,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)individuals.This paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and NC.To improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized VGG-16.We performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed models.Our experimental results demonstrate superior performance in classifying the three binary classification tasks:NC vs.AD,NC vs.MCI,and MCI vs.AD.Specifically,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,respectively.Our study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM scans.Our findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

多模态超声联合人工智能S-Detect技术对BI-RADS 4类乳腺结节的诊断分析

目的:分析多模态超声联合人工智能S-Detect技术对乳腺影像报告数据系统(BI-RADS)4类乳腺结节的诊断价值。方法:回顾性收集2020年9月-2024年3月我院收治的62例乳腺病变患者,统计分析所有患者的临床资料,对比不同诊断方法的诊断结果,以病理诊断为金标准,分析多模态超声联合人工智能S-Detect技术对BI-RADS 4类乳腺结节的诊断效能。结果:恶性乳腺结节的超声造影特征为:高增强、增强方式呈向心性、内部回声不均匀、形态不规则、边缘不清晰、存在周边放射状血管及增强后病灶体积增大。本组62例患者共76个乳腺结节,病理结果显示42个结节为恶性,34个结节为良性;常规超声诊断出34个恶性,敏感度为71.43%,特异度为88.24%,准确率为78.95%;人工智能S-Detect技术诊断出39个恶性,敏感度为76.19%,特异度为79.41%,准确率为77.63%;多模态超声诊断出44个恶性,敏感度为85.71%,特异度为76.47%,准确率为81.58%;多模态超声联合人工智能S-Detect技术诊断出45个恶性,敏感度为95.24%,特异度为85.29%,准确率为90.79%,其中多模态超声联合人工智能S-Detect技术检查的诊断结果与病理结果的一致性最高,Kappa值为0.812。ROC曲线对比分析显示,多模态超声联合人工智能S-Detect技术的诊断效能与单纯常规超声、人工智能S-Detect技术、多模态超声比较均有统计学意义(Z=0.275/2.603/2.083,P=0.023/0.009/0.037)。结论:相较于单一超声、多模态超声、人工智能S-Detect技术,多模态超声联合人工智能S-Detect技术诊断BI-RADS 4类乳腺结节的诊断效能较高,其诊断结果与病理结果高度一致。

DTI and Structural MRI Classification in Alzheimer’s Disease

In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on diffusion tensor (DTI) and anatomical magnetic resonance imaging (MRI). We propose a new multimodal measure that combines anatomical and diffusivity measures at the voxel level. Our approach relies on whole-brain parcellation into 73 anatomical regions and the extraction of multimodal characteristics in these regions. Discriminative features are identified using different feature selection (FS) methods and used in a Support Vector Machine (SVM) for individual classification. Fifteen AD patients and 16 elderly controls were discriminated using mean diffusivity alone, combination of mean diffusivity and fractional anisotropy, and multimodal measures in the 73 ROIs and the overall accuracy obtained was 65.2%, 68.6% and 72% respectively. Overall accuracy reached 99% in multimodal measures when relevant regions were selected.

Clinical Observation and Proposed Classification of Vitiliginous Patches by a Wood’s Lamp

Although vitiligo lesion especially in static state is characterized as sharply demarcated and complete depigmented macule, we encounter patients who have various manners of hypopigmented lesions. We examined the 81 lesions using the newly released Wood’s lamp (Woody®) and investigated whether or not vitiliginous lesions could be uniformly classified under Wood’s lamp illumination and also this classification helped to estimate the tendency of repigmentation after treatment. As result, the vitiliginous lesions were categorized into 4 types on intra- and peri-lesions prior to treatment by using the Wood’s lamp. The inside and border of the lesions were classified as follows: clear white, faint, multi-dot, and perifollicular for the inside, and sharp, blunt, confetti, and trichrome for the border. Suggestive residual pigmentation was detected in 73.6% of patients at the first visit and repigmentation was observed in 67.9% of patients at least 3 months after treatment. Lesions with the “clear white” inside pattern showed significantly lower repigmentation frequency in 38.5% of patients compared to others. The borders with 4 enlarged lesions were composed of 3 of confetti-type and one of sharp-type. This preliminary study demonstrated that detailed observation with a Wood’s lamp could be the basis to classify vitiliginous lesions and might be useful for predicting not only disease progression but also repigmentation prior to treatment.

Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

基于改良UPOINT(S)系统的个体化治疗对CP/CPPS合并SD患者的疗效分析

目的基于改良UPOINT(S)临床表现分型系统,对慢性前列腺炎/慢性盆腔疼痛综合征(CP/CPPS)合并以勃起功能障碍(ED)、早泄(PE)为代表的性功能障碍(SD)患者采用以临床症状为导向的标准化诊断和个体化治疗,验证其临床疗效,并为选择临床治疗方案提供依据。方法选取2018年1月至2021年12月中国人民解放军联勤保障部队第九四○医院门诊治疗的160例CP/CPPS合并以ED和/或PE为代表的SD患者作为研究对象,随机分为常规药物治疗的对照组(n=53)、基于传统UPOINT临床表现分型系统指导治疗的传统组(n=49)和基于改良UPOINT(S)临床表现分型系统指导治疗的改良组(n=58)。根据各组治疗前后美国国立卫生研究院慢性前列腺炎症状指数(NIH-CPSI)评分、最大尿流率(Qmax)、视觉模拟评分法(VAS)评分、国际勃起功能指数5(IIEF-5)评分、中国早泄患者性功能评分表(C-ISFPE)评分、前列腺液中白细胞(EPS-WBC)计数的变化,评价三组临床疗效差异。结果治疗前三组临床资料差异均无统计学意义(P>0.05)。治疗后,与对照组比较,传统组和改良组在所有指标上均改善显著,差异均具有统计学意义(P<0.05);与传统组比较,改良组在治疗ED[IIEF-5评分(22.0±1.2)分vs.(19.8±2.0)分,P=0.006]和PE[C-ISFPE评分(25.5±1.8)分vs.(19.5±2.2)分,P=0.005]方面改善更加显著,差异均具有统计学意义(P<0.05)。结论基于改良UPOINT(S)临床表现分型系统指导的个体化治疗,可显著改善CP/CPPS合并SD患者的性功能异常状况,相较于常规治疗和基于传统UPOINT临床表现分型系统指导治疗的疗效更好。

Multimodal 3D Convolutional Neural Networks for Classification of Brain Disease Using Structural MR and FDG-PET Images

The classification and identification of brain diseases with multimodal information have attracted increasing attention in the domain of computer-aided. Compared with traditional method which use single modal feature information, multiple modal information fusion can classify and diagnose brain diseases more comprehensively and accurately in patient subjects. Existing multimodal methods require manual extraction of features or additional personal information, which consumes a lot of manual work. Furthermore, the difference between different modal images along with different manual feature extraction make it difficult for models to learn the optimal solution. In this paper, we propose a multimodal 3D convolutional neural networks framework for classification of brain disease diagnosis using MR images data and PET images data of subjects. We demonstrate the performance of the proposed approach for classification of Alzheimer’s disease (AD) versus mild cognitive impairment (MCI) and normal controls (NC) on the Alzheimer’s Disease National Initiative (ADNI) data set of 3D structural MRI brain scans and FDG-PET images. Experimental results show that the performance of the proposed method for AD vs. NC, MCI vs. NC are 93.55% and 78.92% accuracy respectively. And the accuracy of the results of AD, MCI and NC 3-classification experiments is 68.86%.