RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

Enhanced characterization of solid solitary pulmonary nodules with Bayesian analysis-based computer-aided diagnosis

The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computeraided diagnosis(CAD) vs human judgment alone in characterizing solitary pulmonary nodules(SPNs) at computed tomography(CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator(BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic(ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions(P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs(15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses(mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.

Computer-aided texture analysis combined with experts' knowledge: Improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease(CD).METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computerbased classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique(MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts visually classified each image as normal mucosa(Marsh-0) or villous atrophy(Marsh-3). The experts' decisions were further integrated into state-of-the-arttexture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts' diagnoses in 27 different settings.RESULTS: Compared to the experts' diagnoses, in 24 of 27 classification settings(consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant(P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95%(P < 0.001).CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.

Computer-aided diagnosis for contrast-enhanced ultrasound in the liver

Computer-aided diagnosis(CAD) has become one of the major research subjects in medical imaging and diagnostic radiology.The basic concept of CAD is to provide computer output as a second opinion to assist radiologists' image interpretations by improving the accuracy and consistency of radiologic diagnosis and also by reducing the image-reading time.To date,research on CAD in ultrasound(US)-based diagnosis has been carried out mostly for breast lesions and has been limited in the fields of gastroenterology and hepatology,with most studies being conducted using B-mode US images.Two CAD schemes with contrast-enhanced US(CEUS) that are used in classifying focal liver lesions(FLLs) as liver metastasis,hemangioma,or three histologically differentiated types of hepatocellular carcinoma(HCC) are introduced in this article:one is based on physicians' subjective pattern classifications(subjective analysis) and the other is a computerized scheme for classification of FLLs(quantitative analysis).Classification accuracies for FLLs for each CAD scheme were 84.8% and 88.5% for metastasis,93.3% and 93.8% for hemangioma,and 98.6% and 86.9% for all HCCs,respectively.In addition,the classification accuracies for histologic differentiation of HCCs were 65.2% and 79.2% for well-differentiated HCCs,41.7% and 50.0% for moderately differentiated HCCs,and 80.0% and 77.8% for poorly differentiated HCCs,respectively.There are a number of issues concerning the clinical application of CAD for CEUS,however,it is likely that CAD for CEUS of the liver will make great progress in the future.

Efficient Computer Aided Diagnosis System for Hepatic Tumors Using Computed Tomography Scans

One of the leading causes of mortality worldwide is liver cancer.The earlier the detection of hepatic tumors,the lower the mortality rate.This paper introduces a computer-aided diagnosis system to extract hepatic tumors from computed tomography scans and classify them into malignant or benign tumors.Segmenting hepatic tumors from computed tomography scans is considered a challenging task due to the fuzziness in the liver pixel range,intensity values overlap between the liver and neighboring organs,high noise from computed tomography scanner,and large variance in tumors shapes.The proposed method consists of three main stages;liver segmentation using Fast Generalized Fuzzy C-Means,tumor segmentation using dynamic thresholding,and the tumor’s classification into malignant/benign using support vector machines classifier.The performance of the proposed system was evaluated using three liver benchmark datasets,which are MICCAI-Sliver07,LiTS17,and 3Dircadb.The proposed computer adided diagnosis system achieved an average accuracy of 96.75%,sensetivity of 96.38%,specificity of 95.20%and Dice similarity coefficient of 95.13%.

Research on Computer Aided Design （CAD） Technique and Novel Pattern for Electronic Architectural Drawing and Perspective

With the progress of computer technology, CAD technique is urgent needed. In this paper, we conduct numerical and theoretical analysis and research on computer aided design technique and novel pattern for electronic architectural drawing and perspective. AUTOCAD drawing soRware because of its versatility and easy entry, in engineering has broad user base, but also because of the basis of general drawing software, often cannot directly use of the existing order efficiently complete professional drawing. We modify the current pattern and introduce our proposed pattern for advancement. The experiment proves the effectiveness of the pattern. In addition, we will conduct more insightful research in the future to nolish the current anoroach.

Complications of CAD/CAM Fabricated Surgical Template for Static Computer-Aided Implant Surgery

Purpose: Surgical templates produced by digital simulation and CAD/CAM allow for three-dimensional control of implant placement. However, due to clinical limitations, there are complications during the use of the template. The purpose of this study was to summarize the complications associated with the use of surgical templates for static computer-aided implant surgery. Methods: Complications were collected during the observation period, and then their implant sites were reanalyzed with simulation software. Results: There were 104 cases during the observation period, 5 cases had complications. Mechanical complications were observed in four cases, including three cases in which the frame of the template fractured during implant placement surgery and one case in which the sleeve fell off the surgical template. In one case, there was an error in the planned position. All cases were mandibular molar cases, and all cases of frame fracture were at the free end defect site. All cases had a Hounsfield unit of more than 700 at the implant site, and some of them had a significantly small jaw opening. Conclusion: Although the spread of CAD/CAM surgical templates has made it possible to avoid problems caused by the position of the implant, it has been difficult to avoid fractures in cases of mandibular free end defects with high Hounsfield unit.

Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses

Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.

Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

Alzheimer’s disease (AD) is a dementing disorder and one of the major public health problems in countries with greater longevity. The cerebral cortical thickness and cerebral blood flow (CBF), which are considered as morphological and functional image features, respectively, could be decreased in specific cerebral regions of patients with dementia of Alzheimer type. Therefore, the aim of this study was to develop a computer-aided classification system for AD patients based on machine learning with the morphological and functional image features derived from a magnetic resonance (MR) imaging system. The cortical thicknesses in ten cerebral regions were derived as morphological features by using gradient vector trajectories in fuzzy membership images. Functional CBF maps were measured with an arterial spin labeling technique, and ten regional CBF values were obtained by registration between the CBF map and Talairach atlas using an affine transformation and a free form deformation. We applied two systems based on an arterial neural network (ANN) and a support vector machine (SVM), which were trained with 4 morphological and 6 functional image features, to 15 AD patients and 15 clinically normal (CN) subjects for classification of AD. The area under the receiver operating characteristic curve (AUC) values for the two systems based on the ANN and SVM with both image?features were 0.901 and 0.915, respectively. The AUC values for the ANN-and SVM-based systems with the morphological features were 0.710 and 0.660, respectively, and those with the functional features were 0.878 and 0.903, respectively. Our preliminary results suggest that the proposed method may have potential for assisting radiologists in the differential diagnosis of AD patients by using morphological and functional image features.

Applications of Computer Aided Modeling Techniques to Complex ICE Components

Design of internal combustion engine (ICE) components is one of the earliest and also the most active areas in which computer aided modeling techniques are applied. Computer aided modeling techniques could provide requisite information for follow up designing segments such as structural analysis, design of technological process and manufacturing etc, and thereby lead to the reduction of product design period and the quality and reliability improvement of ICE components. So the developing situations of ICE components' 2 D drafting, 3 D modeling of ICE, overall CAD of ICE as well as component design expert system etc. are surveyed, which are the typical applications of computer aided modeling techniques in ICE component design process, and some existent problems and tasks are pointed out so as to make some references for the further research work.

Computer Aided Design Technology for Convex Faceted Gem Cuts Based on the Half-Edge Data Structure

Aiming to increase the efficiency of gem design and manufacturing, a new method in computer-aided-design (CAD) of convex faceted gem cuts (CFGC) based on Half-edge data structure (HDS), including the algorithms for the implementation is presented in this work. By using object-oriented methods, geometrical elements of CFGC are classified and responding geometrical feature classes are established. Each class is implemented and embedded based on the gem process. Matrix arithmetic and analytical geometry are used to derive the affine transformation and the cutting algorithm. Based on the demand for a diversity of gem cuts, CAD functions both for free-style faceted cuts and parametric designs of typical cuts and visualization and human-computer interactions of the CAD system including two-dimensional and three-dimensional interactions have been realized which enhances the flexibility and universality of the CAD system. Furthermore, data in this CAD system can also be used directly by the gem CAM module, which will promote the gem CAD/CAM integration.

基于CATIA的钢结构CAD与CAE一体化实现方法

为了应对钢结构在实现计算机辅助设计(CAD)与计算机辅助工程(CAE)一体化时的特殊性所带来的挑战,分析和归纳CAD与CAE一体化中数据转换,与几何理想化等关键问题,在此基础上,提出一种基于CATIA V6软件实现钢结构CAD与CAE一体化的方法,并通过一个绑扎桥钢结构的设计案例验证了该方法的有效性。通过所提方法分别实现了CAE几何理想化的自动切换与整合载荷数据至CAD模型的技术目标,从而避免了基于传统方法所带来的数据分离问题,减少了人工介入的低效工作。

A Computer-aided Design System for Framed-mould in Autoclave Processing

The general computer-aided design （CAD） software cannot meet the mould design requirement of the autoclave process for composites, because many parameters such as temperature and pressure should be considered in the mould design process, in addition to the material and geometry of the part. A framed-mould computer-aided design system （FMCAD） used in the autoclave moulding process is proposed in this paper. A function model of the software is presented, in which influence factors such as part structure, mould structure, and process parameters are considered; a design model of the software is established using object oriented （O-O） technology to integrate the stiffness calculation, temperature field calculation, and deformation field calculation of mould in the design, and in the design model, a hybrid model of mould based on calculation feature and form feature is presented to support those calculations. A prototype system is developed, in which a mould design process wizard is built to integrate the input information, calculation, analysis, data storage, display, and design results of mould design. Finally, three design examples are used to verify the prototype.

Recent advances in computerized imaging and its vital roles in liverdisease diagnosis, preoperative planning, and interventional liversurgery: A review

The earliest and most accurate detection of the pathological manifestations of hepatic diseases ensures effective treatments and thus positive prognostic outcomes.In clinical settings,screening and determining the extent of a pathology are prominent factors in preparing remedial agents and administering approp-riate therapeutic procedures.Moreover,in a patient undergoing liver resection,a realistic preoperative simulation of the subject-specific anatomy and physiology also plays a vital part in conducting initial assessments,making surgical decisions during the procedure,and anticipating postoperative results.Conventionally,various medical imaging modalities,e.g.,computed tomography,magnetic resonance imaging,and positron emission tomography,have been employed to assist in these tasks.In fact,several standardized procedures,such as lesion detection and liver segmentation,are also incorporated into prominent commercial software packages.Thus far,most integrated software as a medical device typically involves tedious interactions from the physician,such as manual delineation and empirical adjustments,as per a given patient.With the rapid progress in digital health approaches,especially medical image analysis,a wide range of computer algorithms have been proposed to facilitate those procedures.They include pattern recognition of a liver,its periphery,and lesion,as well as pre-and postoperative simulations.Prior to clinical adoption,however,software must conform to regulatory requirements set by the governing agency,for instance,valid clinical association and analytical and clinical validation.Therefore,this paper provides a detailed account and discussion of the state-of-the-art methods for liver image analyses,visualization,and simulation in the literature.Emphasis is placed upon their concepts,algorithmic classifications,merits,limitations,clinical considerations,and future research trends.

SonoVCADS在正常胎儿心脏诊断切面显示中的可行性分析

目的探讨基于容积超声图像的心脏计算机辅助系统(SonoVCADS)在正常胎儿心脏诊断切面显示中的可行性。方法选取2021年8月至2022年3月在丽水市中心医院超声科行胎儿心脏检查的66名正常单胎胎儿为研究对象,使用SonoVCADS获得容积超声图像314个。由3名不同经验的观察者对每个诊断切面进行评分,计算SonoVCADS显示诊断切面的成功率和一致性,比较3名观察者使用SonoVCADS显示胎儿心脏诊断切面的时间。另随机选取50名胎儿,比较SonoVCADS与二维超声心动图显示胎儿心脏诊断切面的时间。结果3名观察者使用SonoVCADS显示胎儿心脏8个诊断切面的成功率比较,差异均无统计学意义(均P>0.05)。以观察者B(相对经验不足)为例,70.70%的容积超声图像可以成功显示8个诊断切面,13.69%的容积超声图像可以成功显示7个诊断切面,仅4.15%的容积超声图像成功显示的诊断切面数<4个。观察者B的8个诊断切面的Kappa值为0.652~0.854;3名观察者的8个诊断切面的Kappa值为0.536~0.752。观察者C(检查经验丰富)花费的时间明显少于观察者A和观察者B(均P<0.05)。观察者C使用SonoVCADS显示胎儿心脏8个诊断切面的时间明显短于二维超声心动图(P=0.018)。结论SonoVCADS显示胎儿心脏8个诊断切面的成功率较高,对操作人员的依赖性低,且具有良好的一致性和检查效率。

Improving Thyroid Disorder Diagnosis via Ensemble Stacking and Bidirectional Feature Selection

Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.

Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection

Breast cancer is one of the most common and deadliest types of cancer among women and early detection is of major importance to decrease mortality rates. Microcalcification clusters and masses are two major indicators of malignancy in the early stages of this disease, when mammography is typically used as the screening technology. Computer-Aided Diagnosis (CAD) systems can support the radiologists’ work, by performing a double-reading process, which provides a second opinion that the physician can take into account in the detection process. This paper presents a CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content. A genetic algorithm is used to find the subset of features that provide the greatest discriminant power. Our results show that the SVM presented the highest overall accuracy and specificity for classifying microcalcification clusters, while the NN outperformed the rest for mass-classification in the same parameters. Overall accuracy, sensitivity and specificity were measured.

工程学科常用CAD软件及功能分析

计算机辅助设计(Computer Aided Design,CAD)已经成为机械、电子、航天、化工及建筑等产业中不可或缺的工具之一。概述CAD软件行业的最新发展趋势和我国在该领域的进展,分析CAD软件的技术创新、产业布局以及国内企业在底层技术研发方面的进展。通过调研国际知名CAD软件公司的最新产品和策略以及国内企业的研发成果,分析CAD软件行业的技术动向。结果显示,CAD软件正朝着云端化、集成化、一体化以及智能化的方向发展,国内外企业都在不断探索新技术与CAD软件的结合,以提高设计效率和产品质量。我国CAD软件行业在政策支持和市场需求增长下将迎来更大发展空间,应加强自主研发和创新力度,以推出更多具有自主知识产权和核心竞争力的CAD产品。

CAD/CAM与传统暂时性修复体边缘及内部适合性的体外研究

目的研究CAD/CAM方法与传统方法对暂时性修复体的边缘及内部适合性的影响。方法将60颗全冠预备标准模型树脂牙随机分成四组(n=15):Protemp^(TM)4组、PMMA组、Cemeo组和Upcera PMMA组。制作出相应冠修复体后,将其粘接于相应的标准模型牙上,水浴24 h,沿颊舌向的定位线切开。体视显微镜放大后拍照,利用Imageview显微测量软件对边缘及内部的参考点的间隙进行测量,并进行统计学分析。结果3组CAD/CAM暂时性修复体间边缘适合性、轴壁适合性、轴牙合线角适合性、牙合面适合性两两比较差异无统计学意义(P>0.05),传统暂时性修复体Protemp^(TM)4与3组CAD/CAM暂时性修复体间任意一组边缘适合性、轴壁适合性、轴牙合线角适合性、牙合面适合性两两比较差异均具有统计学意义(P<0.05)。结论CAD/CAM暂时性修复体的边缘及内部(轴壁、轴牙合线角、牙合面)适合性优于传统直接法制作的暂时性修复体。