T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

BACKGROUND The study on predicting the differentiation grade of colorectal cancer(CRC)based on magnetic resonance imaging(MRI)has not been reported yet.Developing a non-invasive model to predict the differentiation grade of CRC is of great value.AIM To develop and validate machine learning-based models for predicting the differ-entiation grade of CRC based on T2-weighted images(T2WI).METHODS We retrospectively collected the preoperative imaging and clinical data of 315 patients with CRC who underwent surgery from March 2018 to July 2023.Patients were randomly assigned to a training cohort(n=220)or a validation cohort(n=95)at a 7:3 ratio.Lesions were delineated layer by layer on high-resolution T2WI.Least absolute shrinkage and selection operator regression was applied to screen for radiomic features.Radiomics and clinical models were constructed using the multilayer perceptron(MLP)algorithm.These radiomic features and clinically relevant variables(selected based on a significance level of P<0.05 in the training set)were used to construct radiomics-clinical models.The performance of the three models(clinical,radiomic,and radiomic-clinical model)were evaluated using the area under the curve(AUC),calibration curve and decision curve analysis(DCA).RESULTS After feature selection,eight radiomic features were retained from the initial 1781 features to construct the radiomic model.Eight different classifiers,including logistic regression,support vector machine,k-nearest neighbours,random forest,extreme trees,extreme gradient boosting,light gradient boosting machine,and MLP,were used to construct the model,with MLP demonstrating the best diagnostic performance.The AUC of the radiomic-clinical model was 0.862(95%CI:0.796-0.927)in the training cohort and 0.761(95%CI:0.635-0.887)in the validation cohort.The AUC for the radiomic model was 0.796(95%CI:0.723-0.869)in the training cohort and 0.735(95%CI:0.604-0.866)in the validation cohort.The clinical model achieved an AUC of 0.751(95%CI:0.661-0.842)in the training cohort and 0.676(95%CI:0.525-0.827)in the validation cohort.All three models demonstrated good accuracy.In the training cohort,the AUC of the radiomic-clinical model was significantly greater than that of the clinical model(P=0.005)and the radiomic model(P=0.016).DCA confirmed the clinical practicality of incorporating radiomic features into the diagnostic process.CONCLUSION In this study,we successfully developed and validated a T2WI-based machine learning model as an auxiliary tool for the preoperative differentiation between well/moderately and poorly differentiated CRC.This novel approach may assist clinicians in personalizing treatment strategies for patients and improving treatment efficacy.

Optimal online algorithms for scheduling on two identical machines under a grade of service

This work is aimed at investigating the online scheduling problem on two parallel and identical machines with a new feature that service requests from various customers are entitled to many different grade of service (GoS) levels, so each job and machine are labelled with the GoS levels, and each job can be processed by a particular machine only when its GoS level is no less than that of the machine. The goal is to minimize the makespan. For non-preemptive version, we propose an optimal online al-gorithm with competitive ratio 5/3. For preemptive version, we propose an optimal online algorithm with competitive ratio 3/2.

Leveraging machine learning techniques for predicting pancreatic neuroendocrine tumor grades using biochemical and tumor markers

BACKGROUND The incidence of pancreatic neuroendocrine tumors (PNETs) is now increasing rapidly. The tumor grade of PNETs significantly affects the treatment strategy and prognosis. However, there is still no effective way to non-invasively classify PNET grades. Machine learning (ML) algorithms have shown potential in improving the prediction accuracy using comprehensive data. AIM To provide a ML approach to predict PNET tumor grade using clinical data. METHODS The clinical data of histologically confirmed PNET cases between 2012 and 2018 were collected. A method of minimum P for the Chi-square test was used to divide the continuous variables into binary variables. The continuous variables were transformed into binary variables according to the cutoff value, while the P value was minimum. Four classical supervised ML models, including logistic regression, support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) were trained by clinical data, and the models were labeled with the pathological tumor grade of each PNET patient. The performance of each model, including the weight of the different parameters, were evaluated. RESULTS In total, 91 PNET cases were included in this study, in which 32 were G1, 48 were G2 and 11 were G3. The results showed that there were significant differences among the clinical parameters of patients with different grades. Patients with higher grades tended to have higher values of total bilirubin, alpha fetoprotein, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4. Among the models we used, LDA performed best in predicting the PNET tumor grade. Meanwhile, MLP had the highest recall rate for G3 cases. All of the models stabilized when the sample size was over 70 percent of the total, except for SVM. Different parameters varied in affecting the outcomes of the models. Overall, alanine transaminase, total bilirubin, carcinoembryonic antigen, carbohydrate antigen 19-9 and carbohydrate antigen 72-4 affected the outcome greater than other parameters. CONCLUSION ML could be a simple and effective method in non-invasively predicting PNET grades by using the routine data obtained from the results of biochemical and tumor markers.

Investigation of Surface-inset Machines with Mixed Grade Magnets Considering Magnet Thickness

This paper presents a mixed grade magnet model for surface-inset machines considering the magnet thickness. In the polar coordinates, on the basis of the Laplace/quasi-Poisson equations and boundary conditions, the constructed matrix equations are solved and the air gap magnetic field in the machine is derived. Taking an 8-pole/12-slot surface-inset motor as an example, through the presented optimization process, the air gap field is optimized considering the magnet thickness, remanence and magnetization angle. In addition, the back-EMF and electromagnetic torque are analytically obtained. The optimized results show that the proposed mixed grade magnet model has larger electromagnetic torque and smaller torque ripple than the conventional one. Finally, the analytical predictions are evaluated by finite element analysis(FEA).

Estimation of sand liquefaction based on support vector machines

The origin and influence factors of sand liquefaction were analyzed, and the relation between liquefaction and its influence factors was founded. A model based on support vector machines (SVM) was established whose input parameters were selected as following influence factors of sand liquefaction: magnitude (M), the value of SPT, effective pressure of superstratum, the content of clay and the average of grain diameter. Sand was divided into two classes: liquefaction and non-liquefaction, and the class label was treated as output parameter of the model. Then the model was used to estimate sand samples, 20 support vectors and 17 borderline support vectors were gotten, then the parameters were optimized, 14 support vectors and 6 borderline support vectors were gotten, and the prediction precision reaches 100%. In order to verify the generalization of the SVM method, two other practical samples' data from two cities, Tangshan of Hebei province and Sanshui of Guangdong province, were dealt with by another more intricate model for polytomies, which also considered some influence factors of sand liquefaction as the input parameters and divided sand into four liquefaction grades: serious liquefaction, medium liquefaction, slight liquefaction and non-liquefaction as the output parameters. The simulation results show that the latter model has a very high precision, and using SVM model to estimate sand liquefaction is completely feasible.

Evaluating soil nutrients of Dacrydium pectinatum in China using machine learning techniques

Background: The accurate estimation of soil nutrient content is particularly important in view of its impact on plant growth and forest regeneration. In order to investigate soil nutrient content and quality for the natural regeneration of Dacrydium pectinatum communities in China, designing advanced and accurate estimation methods is necessary.Methods: This study uses machine learning techniques created a series of comprehensive and novel models from which to evaluate soil nutrient content. Soil nutrient evaluation methods were built by using six support vector machines and four artificial neural networks.Results: The generalized regression neural network model was the best artificial neural network evaluation model with the smallest root mean square error(5.1), mean error(-0.85), and mean square prediction error(29). The accuracy rate of the combined k-nearest neighbors(k-NN) local support vector machines model(i.e. k-nearest neighbors-support vector machine(KNNSVM)) for soil nutrient evaluation was high, comparing to the other five partial support vector machines models investigated. The area under curve value of generalized regression neural network(0.6572) was the highest, and the cross-validation result showed that the generalized regression neural network reached 92.5%.Conclusions: Both the KNNSVM and generalized regression neural network models can be effectively used to evaluate soil nutrient content and quality grades in conjunction with appropriate model variables. Developing a new feasible evaluation method to assess soil nutrient quality for Dacrydium pectinatum, results from this study can be used as a reference for the adaptive management of rare and endangered tree species. This study, however, found some uncertainties in data acquisition and model simulations, which will be investigated in upcoming studies.

基于改进ResNet18的干香菇等级识别

为解决干香菇等级识别技术复杂及识别精度不高的问题,提出了一种基于残差神经网络ResNet18的干香菇等级识别方法。首先将传统的ResNet18中Stem的7×7卷积层替换为3个3×3卷积层串联,保证在感受野保持不变的情况下进一步减小计算量;其次针对残差块中线性变换和非线性变换不足的问题,引入融合非对称卷积和h-swish激活函数,增加了模型的复杂性,使其能够进行更深层次的特征学习;最后在ResNet18骨干网络中引入高效通道注意力机制,加强模型提取特征的能力。实验结果表明,改进后的ResNet18网络模型准确度达97.04%,相比ResNet18网络模型方法提升了4.81%,且性能优于VGG16、MobileNetV2、DenseNet121、ResNet34等网络模型方法,可提高干香菇等级的识别精度,单幅图像的检测时间为5.91 ms,对干香菇智能分拣过程中的等级识别具有借鉴意义。

基于机器视觉技术的鸡蛋新鲜度智能分级系统

为了对鸡蛋的新鲜度进行智能、便捷、无损、客观的检测,设计了基于机器视觉技术的鸡蛋新鲜度自动分级系统.系统利用图像处理算法智能读取电子秤显示的鸡蛋重量、测算鸡蛋的外部物理特征(纵径、横径、体积);由重量和体积计算密度,根据密度变化与储存时间之间的关系进行鸡蛋新鲜度的智能评价和分级.将87枚同一时间产出的鸡蛋样本作为模型集用于实验建模,40枚存放时间不同的鸡蛋样本作为测试集用于测试系统的可靠性.测试结果显示系统对测试集鸡蛋新鲜度智能分级的平均正确率为90.3%,可对禽蛋各项参数的智能检测提供技术支持.

基于ISSA-HKLSSVM的浮选精矿品位预测方法

针对浮选过程变量滞后、耦合特征及建模样本数量少所导致精矿品位难以准确预测的问题,提出了一种基于改进麻雀搜索算法(Improved Sparrow Search Algorithm,ISSA)优化混核最小二乘支持向量机(Hybrid Kernel Least Squares Support Vector Machine,HKLSSVM)的浮选过程精矿品位预测方法.首先采集浮选现场载流X荧光品位分析仪数据作为建模变量并进行预处理,建立基于最小二乘支持向量机(Least Squares Support Vector Machine,LSSVM)的预测模型,以此构建新型混合核函数,将输入空间映射至高维特征空间,再引入改进麻雀搜索算法对模型参数进行优化,提出基于ISSA-HKLSSVM方法实现精矿品位预测,最后开发基于LabVIEW的浮选精矿品位预测系统对本文提出方法实际验证.实验结果表明,本文提出方法对于浮选过程小样本建模具有良好拟合能力,相比现有方法提高了预测准确率,可实现精矿品位的准确在线预测,为浮选过程的智能调控提供实时可靠的精矿品位反馈信息.

基于自动机器学习的岩爆烈度分级预测模型

为了解决岩爆预测过程中人为因素影响过大与预测时间过长的问题,提出了一种基于自动机器学习的岩爆烈度分级预测模型。收集国内外岩爆案例样本构建数据,基于5个自动机器学习模型框架训练岩爆烈度分级预测模型,采用准确率、精确度、召回率、F1指标评价模型性能。与13种常见机器学习模型预测结果进行对比分析,得出AutoML框架构建的岩爆预测模型预测准确率远远高于13种传统机器学习算法构建的岩爆预测模型。其中,基于Auto-Sklearn框架构建的岩爆预测模型准确率高达0.969,基于Auto-Gluon框架构建的岩爆预测模型准确率在5个框架中最低,准确率也高达0.927。应用构建的模型预测晒旗河磷矿的岩爆发生情况,预测结果与现场情况一致,表明基于自动机器学习的岩爆烈度分级预测模型能够有效预测实际工程中的岩爆发生情况。

结合机器视觉和光谱技术的番茄综合品质检测方法

[目的]实现番茄内外部品质同时快速准确测定,提高番茄的分级效率和品质。[方法]基于机器视觉和光谱技术,提出一种结合外部品质和内部品质的番茄综合品质分级方法。通过对YOLOv8模型进行4个方面的优化(轻量化卷积、小目标检测层、CBAM注意机制和损失函数)完成外部缺陷检测,结合果形指数和番茄尺寸完成外部品质分级。通过预处理方法、特征提取方法和改进粒子群优化的最小二乘支持向量机完成番茄内部品质分级。通过试验对所提分级检测方法的性能进行分析。[结果]所提方法可以实现番茄综合品质检测,具有较高的准确性和效率。外部品质分级准确率>93.00%,内部品质分级准确率>86.00%,融合品质分级准确率>96.00%,平均分级时间<0.25 s。[结论]结合机器视觉和光谱检测技术可以实现番茄综合品质的快速、无损和准确评估。

基于改进鲸鱼优化CNN的红富士苹果外观分级方法

目的:有效提升机器视觉技术对红富士苹果外观品质分级的准确率。方法:建立不同外观品质等级的红富士苹果图像数据库,通过对数据库图像进行图像增强预处理,以提高模型训练效果和泛化能力。构造改进鲸鱼优化CNN模型,采用加权灰色关联度法压缩CNN卷积规模,以降低特征间的冗余度干扰和提高模型的运算速度;利用改进的鲸鱼优化算法对模型超参数进行优化配置,以降低超参数配置不当对模型分级结果的影响。结果:试验所提分级方法准确率更高,分级精确度、灵敏度分别提高了2.05%,2.46%。结论:试验方法能够有效实现对红富士苹果的外观分级。

基于机器视觉的咖啡生豆快速检测与分级研究

目的建立基于机器视觉的咖啡生豆快速检测分级方法,实现咖啡生豆的快速无损等级评估。方法本研究提取咖啡生豆图像预处理后的形状和颜色共13种特征,首先通过特征分布图分析与主成分分析找到13类缺陷豆的显著特征,并确定其显著特征值范围来判定缺陷豆,然后对正常豆按粒度大小判断其等级,最后在MATLAB App Designer平台上设计了咖啡生豆快速检测与分级界面。结果利用机器视觉技术能很好地识别各个类别咖啡生豆,检测一级咖啡生豆准确率为94.77%、二级为93.20%、三级为95.85%,13类缺陷豆平均准确率为82.25%,咖啡生豆检测平均准确率达到91.52%,检测300 g咖啡生豆总用时25.3156 s。结论本方法提高了咖啡生豆分级的智能化水平,分级过程平稳且快速,为今后咖啡生豆在线检测分选提供了技术支持。

基于SPA-SVR模型的LIBS铁精矿矿浆中铁品位的在线测量

浮选是选矿过程中的重要一步,浮选过程中矿浆品位是选矿工艺需要实时掌握的重要指标。实验室开发了基于激光诱导击穿光谱的在线矿浆成分分析仪SIA-LIBSlurry,可以通过采集光谱数据对浮选过程中矿浆的各元素含量进行实时测量。但铁矿浆光谱数据维度较高,数据间强烈的多重共线性和非线性问题增加了建模的复杂度。为了解决该问题,本工作比较了两种变量选择算法:竞争性自适应重加权算法(CARS)和连续投影算法(SPA),并结合支持向量机回归(SVR)建立定量分析模型。研究结果表明:全谱6116个变量建立的SVR模型精度较低,预测均方根误差为1.45%;CARS筛选出的231个变量建立的CARS-SVR模型的预测能力有所提高,预测均方根误差为1.09%;SPA筛选出的12个变量建模,SPA-SVR模型取得了最佳预测效果,预测均方根误差降到了0.97%。说明SPA-SVR模型具有较高的预测准确率,有助于提高SIA-LIBSlurry分析仪在线监测的准确性。

阶梯输送式茶叶采收分级一体机设计与试验

针对名优茶机械化采摘效率较低的情况,该研究利用不同芽叶类型的长度差设计了一种阶梯输送式茶叶采收分级一体机,其可在高效采摘的同时实现芽叶分级。首先通过理论分析与初步试验确定了影响一体机采摘新梢完整率与分级成功率的影响因素,并运用Design-Expert软件进行了四因素三水平的Box-Behnken试验,确定了对采摘新梢完整率影响较大的因素为刀片往复频率、刀片纵向距离、皮带主动轮转速,对分级成功率影响较大的因素为刀片纵向距离、皮带间的高度差。进一步以采摘新梢完整率与分级成功率最大为目标对各参数进行优化,得到一体机的最佳参数为刀片往复频率1600次/min、刀片纵向距离12.5 mm、皮带主动轮转速800 r/min、皮带间的高度差31.5 mm。最后使用优化后的参数开展采摘试验,试验结果表明阶梯输送式茶叶采收分级一体机能够高效完成芽叶采摘与分级工作,采摘新梢完整率与分级成功率分别达到77.9%和88.7%,试验值与模型预测值误差小于5%。该研究可为采收分级一体化技术提供理论依据,为提高茶叶机械化采收水平提供技术支撑。

基于磁共振影像组学和语义特征对高级别胶质瘤和转移瘤的鉴别研究

目的本研究旨在结合传统MRI序列及增强检查,提取多模态高通量影像组学特征并联合语义特征,使用不同的机器学习分类器构建不同的模型并绘制列线图来鉴别高级别胶质瘤(high-grade glioma,HGG)和单发性脑转移瘤(solitary brain metastasis,SBM)。材料与方法本研究对101名患者的多参数MR图像进行了回顾性分析,由两位资深医师标定肿瘤感兴趣区,然后对每个序列分别提取影像组学特征后进行组合,共提取428组影像组学特征。为消除人为标定差异,进行组内相关系数一致性检验,并运用最大相关最小冗余算法选取最具相关性的特征,然后进一步通过最小绝对收缩和选择算子算法筛除冗余特征。本研究采用支持向量机、逻辑回归、随机森林及K近邻四种算法建立分类模型。结合放射科医生评估的七项语义特征,通过卡方检验和多因素分析去除差异无统计学意义的语义特征。然后结合组学特征建立综合模型并绘制列线图。最终,评价各模型的诊断能力,以确定最优分类器。结果HGG及SBM患者建立的影像组学模型中LR的受试者工作特征曲线下面积(area under the curve,AUC)值最高,训练集与测试集分别为0.90和0.90。语义特征建立的模型中随机森林模型性能最好,训练集和测试集AUC分别为0.82和0.87。语义特征联合影像组学评分后采用逻辑回归建立的模型性能最好,训练集和测试集AUC分别为0.91和0.92。结论本研究使用影像组学机器学习分类器并联合其他图像语义特征绘制列线图对HGG及SBM进行鉴别,这是一种非侵入性方法,具有较好的准确性,为临床决策和实践提供了较大的帮助。

基于MRI影像组学联合临床特征的机器学习模型预测宫颈鳞癌组织学分级的价值

目的探讨基于MRI影像组学联合临床特征的不同机器学习模型对宫颈鳞癌组织学分级的预测价值。方法回顾性分析经病理活检证实的150例宫颈鳞癌患者,按4∶1的比例随机分为训练集和验证集。从T2加权像脂肪抑制序列(FS-T2WI)和增强T1WI(延迟期)的感兴趣区中提取特征。经过降维和筛选特征后,使用Logistic回归(LR)、支持向量机(SVM)、贝叶斯(NB)、随机森林(RF)、轻量级梯度提升机(LightGBM)、K-最近邻法(KNN)构建预测宫颈鳞癌组织学分级的影像组学模型。采用受试者操作特征(ROC)曲线下面积(AUC)评估6种模型的预测性能。采用单因素及多因素Logistic回归分析预测独立危险因素,并建立临床及影像组学联合模型。通过AUC比较各个模型的差异,决策曲线(DCA)评估模型的临床价值。结果在影像组学模型中,LightGBM模型AUC下面积最大(训练集为0.910,验证集为0.839)。临床特征联合LightGBM模型的AUC面积最大(训练集0.935,验证集0.888),高于临床模型(AUC训练集为0.762,验证集为0.710)和LightGBM影像组学模型。结论LightGBM模型在影像组学模型中预测价值较高。联合模型的DCA效果最佳,具有最好的临床净获益。结合影像组学和临床特征的联合预测模型对宫颈鳞癌低分化具有良好的预测价值,可为临床决策提供一种无创、高效的方法。

基于机器视觉的盆栽微型月季品质分级方法研究

建立准确高效的品质分级方法,对于花卉产业的标准化发展具有重要的意义。针对人工分级劳动强度大、经验要求高、缺乏统一性等问题,提出了基于机器视觉技术的盆栽微型月季品质分级方法。该方法根据盆栽微型月季的株高、花朵数、整齐度、花盖度和病虫害状况特征的不同,分别提取其相应的特征参数,并利用最小二乘支持向量机作为分类器,对其品质等级进行评价。经试验研究,单独利用株高、花朵数、整齐度、花盖度和病虫害状况特征进行分级的准确率分别为95.65%、94.68%、94.68%、94.20%、96.61%,而综合特征分级准确率为99.50%,验证了特征提取和分类模型的有效性,利用综合特征进行分级时间为10 s,明显提高了分级效率,该方法为建立准确高效的智能盆栽微型月季分级方法提供了理论参考。

基于贝叶斯统计推断的粗集料级配测试研究

拌合站现场粗集料来料的检测一般是通过筛网筛分的方式进行,检测效率低下。因此,引入一种基于贝叶斯统计推断的机器视觉检测方法,实现对集料的快速筛分。利用工业相机采集下落过程中的集料颗粒并进行图像预处理,选取每颗集料的图像序列中最大的Feret短径作为图像特征。此特征为集料颗粒的局部特征,无法代表颗粒的全局三维特征。因此引入贝叶斯统计推断的方法,首先利用贝叶斯公式计算出每颗集料在不同实际粒径区间的后验概率,然后通过概率累加获得被测集料在不同集料规格的可能颗粒数量。为推断每颗集料级配占比,利用贝叶斯推断的方法计算后验分布,从而估计集料颗粒的占比情况。实验结果表明,预测粒径与实际粒径的分布占比最大偏差为1.67%,符合现场快速检测要求。

基于机器视觉的水果外观品质检测研究进展

水果中富含多种营养成分,随着经济和社会生活水平的提高,高品质水果越来越受人们的青睐,其外观品质已经成为影响消费者采购的重要因素。早期我国主要依赖人工对水果进行分级,效率和准确率较低,成本和工人劳动强度较大。近年来随着机器视觉技术的不断发展,大量的学者将视觉技术应用到水果外观品质的检测中,这种技术具有无损坏、低成本、高效率和操作方便等优点。本文结合国内外学者的研究成果,梳理了机器视觉在水果外观颜色、形状、大小、缺陷和纹理检测中的应用,着重介绍了缺陷提取和分类器对水果识别算法的研究进展,分析了传统视觉分级、机器学习和深度学习的应用特点,提出了机器视觉技术存在的问题并对未来发展趋势进行了展望,以期为水果外观品质检测研究提供参考与借鉴。