白细胞自动分类的一种新方法

本文报道了一个关于白细胞自动分析的新方法。在这一领域的早期研究中,细胞图象的分割被认为是一系列算法中最困难和最有争议的步骤。本文描述的新方法排除了这种算法即寻找核边界和细胞边界的必要性,因而降低了在自动分类过程中对于诸如细胞内部的颗粒的影响、细胞与细胞的相互接触等干扰因素的敏感性。实验利用多灰度阈值法,所得到的分类准确率达91.8％(8类细胞问题,279个试验细胞,包括4类未成熟的细胞)。本文还论及我国开创的泛系方法论对多灰度阈值法的设计构思的启迪和影响。     

Performance characteristics of an artificial intelligence based on convolutional neural network for screening conventional Papanicolaou-stained cervical smears

BackgroundThe conventional Papanicolaou-stained cervical smear is the most common screening test for cervical cancer. The sensitivity of the test in detecting abnormal cells is 67–75% in various studies. Owing to the volume of smears at cancer screening centres, significant man-hours are expended in the test. We have developed a software program for identification of foci of abnormal cells from conventional smears. We have chosen the convolutional neural network (CNN) model for its efficacy in image classification.MethodsA total of 1838 microphotographs from cervical smears, containing 1301 ‘normal’ foci and 537 ‘abnormal’ foci were included in the study. The data set was split into training, testing and validation sets. A CNN was developed in the Python programming language. The CNN was trained with the training and testing set. At the end of training, 94.64% accuracy was achieved in the testing set. The CNN was then run on the validation set (441 images).ResultsThe CNN showed 94.28% sensitivity, 96.01% specificity, 91.66% positive predictive value and 97.30% negative predictive value. The CNN could recognise normal squamous cells, overlapping cells, neutrophils and debris and classify the focus appropriately. False positives were reported when the CNN failed to recognise overlapping cells (2.7% microphotographs). It could correctly label cell clusters with high nuclear cytoplasmic ratio and hyperchromasia. In 1.8% of microphotographs, a false negative was reported.ConclusionThe CNN showed 95.46% diagnostic accuracy, suggesting potential use in screening.     

小儿急性白血病骨髓有核细胞的形态定量分析

应用计算机图像分析系统对３９例小儿急性白血病及８例对照组骨髓涂片中的有核细胞进行形态定量测定。选择１１个　参数作各不同组间的差别显著性检验，采用Ｂａｙｅｓ准则逐步判别分析，建立判别函数式。结果：（１）除细胞形状参数外，其余１０个参数（涉及细胞面积、核面积、核面积与细胞面积比、胞浆面积及核的形状）在对照组与白血病之间及各白血病亚型之间均存在着不同程度的显著性差别．（２）３组判别函数式（对照组、ＡＬＬ、ＡＭＬ间的判别；Ｌ１、Ｌ２、Ｌ３间的判别；Ｍ２、Ｍ３、Ｍ３０、Ｍ５６、Ｍ６间的判别）具有满意的判别分类效果，四代准确率均在９０％以上。     

Study on Contribution of Biological Interpretable and Computer-Aided Features Towards the Classification of Childhood Medulloblastoma Cells

Diagnosis and Prognosis of brain tumour in children is always a critical case. Medulloblastoma is that subtype of brain tumour which occurs most frequently amongst children. Post-operation, the classification of its subtype is most vital for further clinical management. In this paper a novel approach of pathological subtype classification using biological interpretable and computer-aided textural features is forwarded. The classifier for accurate features prediction is built purely on the feature set obtained by segmentation of the ground truth cells from the original histological tissue images, marked by an experienced pathologist. The work is divided into five stages: marking of ground truth, segmentation of ground truth images, feature extraction, feature reduction and finally classification. Kmeans colour segmentation is used to segment out the ground truth cells from histological images. For feature extraction we used morphological, colour and textural features of the cells followed by feature reduction using Principal Component Analysis. Finally both binary and multiclass classification is done using Support Vector Method (SVM). The classification was compared using six different classifiers and performance was evaluated employing five-fold cross-validation technique. The accuracy achieved for binary and multiclass classification before applying PCA were 95.4 and 62.1% and after applying PCA were 100 and 84.9% respectively. The run-time analysis are also shown. Results reveal that this technique of cell level classification can be successfully adopted as architectural view can be confusing. Moreover it conforms substantially to the pathologist’s point of view regarding morphological and colour features, with the addition of computer assisted texture feature.     

SIMCA法在辅助男性生育力检查评价中的应用研究

目的：探讨ＳＩＭＣＡ法在辅助男性生育力检查评价中的应用可能。方法：用ＳＩＭＣＡ法对临床精液检查８个指标的结果进行综合分析评价。结果：本法分类正确率对训练集样本为９８．９％，对预示集为９７．８％。结论：ＳＩＭＣＡ法能有效鉴别两类样本     

支持向量机在CT鉴别诊断肾脏上皮样血管平滑肌脂肪瘤与肾透明细胞癌中的应用

目的探讨支持向量机在CT鉴别诊断肾脏上皮样血管平滑肌脂肪瘤(epithelioid angiomyolipoma,EAML)的CT与肾透明细胞癌(clear cell renal cell carcinoma,cc RCC)中的应用价值。方法搜集70例经病理证实的肾脏肿瘤(EAML、cc RCC病变各35例),采用支持向量机法综合分析其CT特征表现,判定其所属类型。结果支持向量机法(support vector machine,SVM)对EAML病变的诊断正确率为100%;对cc RCC病变的诊断正确率为94.59%;总体平均判别正确率为97.14%;训练集诊断正确率为97.30%;测试集诊断正确率为96.97%;与bagging和adaboost分类算法诊断符合率相接近。结论支持向量机法有助于CT鉴别诊断EAML和cc RCC,可用于辅助日常阅片工作,尤其是年轻医师或基层医院医师的工作。     

Automatic classification of mammography reports by BI-RADS breast tissue composition class

Because breast tissue composition partially predicts breast cancer risk, classification of mammography reports by breast tissue composition is important from both a scientific and clinical perspective. A method is presented for using the unstructured text of mammography reports to classify them into BI-RADS breast tissue composition categories. An algorithm that uses regular expressions to automatically determine BI-RADS breast tissue composition classes for unstructured mammography reports was developed. The algorithm assigns each report to a single BI-RADS composition class: 'fatty', 'fibroglandular', 'heterogeneously dense', 'dense', or 'unspecified'. We evaluated its performance on mammography reports from two different institutions. The method achieves >99% classification accuracy on a test set of reports from the Marshfield Clinic (Wisconsin) and Stanford University. Since large-scale studies of breast cancer rely heavily on breast tissue composition information, this method could facilitate this research by helping mine large datasets to correlate breast composition with other covariates.     

浆膜腔积液细胞病理学诊断阳性病例分析

目的探讨恶性浆膜腔积液细胞学分型诊断与病理学分型诊断偏差的原因。方法235例恶性浆膜腔积液标本,行HE染色后显微镜观察,细胞学分型诊断并对积液里常见的转移癌(肺腺癌、肺鳞癌、肺小细胞未分化癌、卵巢腺癌、胃癌、乳腺癌)进行癌细胞总量等14项细胞形态学指标观察。结果①细胞学分型与病理学分型不符情况:5例中、低分化鳞癌和1例大细胞未分化癌细胞学误诊为腺癌,5例可疑癌胸水及4例可疑癌腹水由于涂片中恶性细胞数量少或癌细胞不典型而未能分型诊断。②不同原发部位及不同病理类型来源的癌细胞在积液涂片各有一些特点:卵巢癌细胞数量较多且以大细胞群存在(占86.11%),肺腺癌、肺小细胞未分化癌及乳腺癌以中、小细胞群为多见;胃癌有表现为单个散在细胞(44.83%),部分病例有成群或成团(55.17%);肺鳞癌多以单个散在为主。结论浆膜腔积液细胞学诊断及观察指标能较好地鉴别各种肿瘤细胞的形态特征,为临床确诊原发恶性肿瘤提供有价值的诊断依据。     

Towards Automatic Recognition of Scientifically Rigorous Clinical Research Evidence

The growing numbers of topically relevant biomedical publications readily available due to advances in document retrieval methods pose a challenge to clinicians practicing evidence-based medicine. It is increasingly time consuming to acquire and critically appraise the available evidence. This problem could be addressed in part if methods were available to automatically recognize rigorous studies immediately applicable in a specific clinical situation. We approach the problem of recognizing studies containing useable clinical advice from retrieved topically relevant articles as a binary classification problem. The gold standard used in the development of PubMed clinical query filters forms the basis of our approach. We identify scientifically rigorous studies using supervised machine learning techniques (Naïve Bayes, support vector machine (SVM), and boosting) trained on high-level semantic features. We combine these methods using an ensemble learning method (stacking). The performance of learning methods is evaluated using precision, recall and F₁ score, in addition to area under the receiver operating characteristic (ROC) curve (AUC). Using a training set of 10,000 manually annotated MEDLINE citations, and a test set of an additional 2,000 citations, we achieve 73.7% precision and 61.5% recall in identifying rigorous, clinically relevant studies, with stacking over five feature-classifier combinations and 82.5% precision and 84.3% recall in recognizing rigorous studies with treatment focus using stacking over word + metadata feature vector. Our results demonstrate that a high quality gold standard and advanced classification methods can help clinicians acquire best evidence from the medical literature.     

GB/T 38327-2019《健康信息学中医药数据集分类》国家标准适用性评价研究

目的 对GB/T 38327-2019《健康信息学中医药数据集分类》国家标准(以下简称"本标准")的适用性进行评价,从用户角度探索对本标准进行评价的方法.方法 本研究采用文献调查法、对比验证法等,选取6名测试人员对120个中医药数据集进行分类验证,与本标准制订人员进行一致性对比分析.结果 测试人员与本标准制订人员分类平...     

基于迁移学习的全连接神经网络舌象分类方法

目的 针对深度学习在舌象分类中训练数据量大、训练设备要求高、训练时间长等问题,提出一种基于迁移学习的全连接神经网络小样本舌象分类方法。方法 应用经ImageNet海量数据集训练后的卷积Inception_v3网络提取舌象点、线等有效特征,再使用全连接神经网络对特征进行训练分类,将深度学习网络学习到的图像知识迁移到舌象识别任务中。利用舌象数据集进行训练、测试。结果 与典型舌象分类方法K最近邻（KNN）算法、支持向量机（SVM）算法和卷积神经网络（CNN）深度学习方法相比,本实验使用的两种方法（Inception_v3+2NN和Inception_v3+3NN）具有较高的舌象分类识别率,准确率分别达90.30%和93.98%,且样本训练时间明显缩短。结论 与KNN算法、SVM算法和CNN深度学习方法相比,基于迁移学习的全连接神经网络舌象分类方法可有效提高舌象分类的准确率、缩短网络的训练时间。     

Semi-supervised Long-tail Endoscopic Image Classification

ObjectiveTo explore the semi-supervised learning (SSL) algorithm for long-tail endoscopic image classification with limited annotations.MethodWe explored semi-supervised long-tail endoscopic image classification in HyperKvasir, the largest gastrointestinal public dataset with 23 diverse classes. Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling. After splitting the training dataset and the test dataset at a ratio of 4:1, we sampled 20%, 50%, and 100% labeled training data to test the classification with limited annotations.ResultsThe classification performance was evaluated by micro-average and macro-average evaluation metrics, with the Mathews correlation coefficient (MCC) as the overall evaluation. SSL algorithm improved the classification performance, with MCC increasing from 0.8761 to 0.8850, from 0.8983 to 0.8994, and from 0.9075 to 0.9095 with 20%, 50%, and 100% ratio of labeled training data, respectively. With a 20% ratio of labeled training data, SSL improved both the micro-average and macro-average classification performance; while for the ratio of 50% and 100%, SSL improved the micro-average performance but hurt macro-average performance. Through analyzing the confusion matrix and labeling bias in each class, we found that the pseudo-based SSL algorithm exacerbated the classifier's preference for the head class, resulting in improved performance in the head class and degenerated performance in the tail class.ConclusionSSL can improve the classification performance for semi-supervised long-tail endoscopic image classification, especially when the labeled data is extremely limited, which may benefit the building of assisted diagnosis systems for low-volume hospitals. However, the pseudo-labeling strategy may amplify the effect of class imbalance, which hurts the classification performance for the tail class.     

比较胸腹水液基细胞学剩余标本制作细胞块的3种方法

目的 优化胸腹水液基细胞学剩余标本制作细胞块的程序,并探讨其在病理诊断中的应用价值.方法 150例胸腹水薄层液基细胞学技术(thinprep cytologic test,TCT)检测剩余标本,根据细胞学诊断结果分为3组,每组50例,分别采用直接离心法、蛋清液作为支架法和细胞块试剂盒法处理后制作石蜡细胞块,对比分析3种不同方法制成的细胞切片上的恶性细胞检出率、细胞分布状况和形态特征,并初步比较细胞块和组织块免疫化学染色效果.结果 150例胸腹水TCT检测出的恶性细胞率为31.3％(47/150),其剩余标本经细胞块法检测出的总恶性细胞检出率为40.7％(61/150),其中直接离心法、蛋清液作为支架法和细胞块试剂盒法的检出率分别为26.0％(13/50)、46.0％(23/50)和50.0％(25/50).用蛋清液作为支架及细胞块试剂盒两种方法制作的细胞块,恶性细胞检出率高于直接离心法(P＜0.05),细胞聚集度和细胞分布也优于直接离心法,细胞块和组织块免疫化学染色效果相似.结论 用蛋清液作为支架及细胞块试剂盒两种方法制作的细胞块可提高胸腹水液基细胞学剩余标本的恶性细胞检出率,并可用于免疫细胞化学检测.     

Mixture Model Segmentation System for Parasagittal Meningioma brain Tumor Classification based on Hybrid Feature Vector

Meningioma is the one of the most common type of brain tumor, it as arises from the meninges and encloses the spine and the brain inside the skull. It accounts for 30% of all types of brain tumor. Meningioma’s can occur in many parts of the brain and accordingly it is named. In this paper, a mixture model based classification of meningioma brain tumor using MRI image is developed. The proposed method consists of four stages. In the first stage, with respect to the cells’ boundary, it is necessary to further processing, which ensures the boundary of some cells is a discrete region. Mathematical Morphology brings a fancy result during the discrete processing. Accurate cancer cell nucleus segmentation is necessary for automated cytological image analysis. Thresholding is a crucial step in segmentation..An adaptive binarization technique is an important step for medical image analysis.Finally, a novel hybrid Fuzzy SVM is designed in the classification stage meningioma brain tumor. The tumor classification results of proposed feature extraction with SVM is 74.24%, MM with FSVM is 82.67% and MM with RBF is 62.71% and our proposed method MM with Hybrid SVM is 91.64%.     

应用多灰度阈值细胞图象分割法自动识别十类白细胞

本文报道应用多灰度阈值细胞图象分割法(MSTS)对严格按照随机原则选择的10类白细胞(其中包括5类幼稚细胞)进行自动识别试验的结果。被测细胞总数1149个。如果不计由于研究者本身所造成的误分,那么单纯由计算机造成的误分率仅为6.4％。MSTS摒弃了寻找细胞和细胞核边界这一至今仍不可靠的算法,用自动的细胞图象分割过程替代传统的半自动(对话式)的边界搜索。实验结果表明这一新技术提供了临床快速定量白细胞自动分析的可能性。     

一元线性回归模型设计及C++实现

针对统计方法中一元线性回归问题,详细描述了其建模方法。根据建模方法设计了一元线性回归模型类,同时给出了统一建模语言(UML)类图;再利用C++语言实现了一元线性回归模型类:最后给出了测试实例,测试结果表明了本文设计以及实现的一元线性回归模型类具有较好的可靠性以及较高的精度。为工程应用奠定基础。     

机器学习在调强放疗计划剂量验证中的作用

目的：探讨机器学习对调强放疗计划剂量验证中的作用。方法：选取2019年3月至2020年8月在温州医科大学附属第一医院接受双弧容积调强放射治疗（VMAT）的141例患者，提取调强计划的13个复杂度参数并收集不同条件下的伽玛通过率（GPR），将数据按照7:3随机划分为训练集与测试集。通过Pearson相关性分析和套索回归（LASSO）筛选参数，利用支持向量机的机器学习方法进行建模，对GPR分别进行数值和分类预测。均方根误差（RMSE）和平均绝对误差（MAE）用来评估模型数值预测的准确性，曲线下面积（AUC）用来评估模型分类的准确性。结果：在GPR数值预测中，在3%/3 mm、3%/2 mm、2%/2 mm条件下，测试集中RMSE分别为2.22、3.51、4.59；MAE分别为1.56、2.68、3.67。在GPR分类预测中，在3%/3 mm、3%/2 mm、2%/2 mm条件下测试集的AUC结果分别0.79、0.78、0.77。结论：基于机器学习对调强放疗计划进行剂量验证具有一定的临床应用价值，为质量保证提供了一种新思路。     

径向基人工神经网络在宫颈细胞图像识别中的应用

目的：探讨径向基（RBF）人工神经网络在宫颈细胞图像识别中的应用。方法：提取宫颈细胞和细胞核的15个形态学特征参数及12个色度学特征参数，对700个宫颈细胞按正常、低度鳞状上皮内病变（LSIL）、高度鳞状上皮内病变（HSIL）、宫颈癌进行分类识别。利用软件STATISTICA7．0建立网络模型并训练，用VC＋＋．NET语言调用网络。结果：RBF网络对训练集的拟合度为97．3％，对测试集的分类准确率为95．4％。在测试集中，正常细胞的识别率为96％，LSIL细胞识别率为94％，HSIL细胞识别率为100％，癌细胞识别率为88％。RBF网络输入参数的敏感度排序与细胞病理学特征基本一致。结论：RBF人工神经网络可以很好的对宫颈细胞特别是HSIL细胞进行分类识别。     

Evaluating resampling methods and structured features to improve fall incident report identification by the severity level

ObjectiveThis study aims to improve the classification of the fall incident severity level by considering data imbalance issues and structured features through machine learning.Materials and MethodsWe present an incident report classification (IRC) framework to classify the in-hospital fall incident severity level by addressing the imbalanced class problem and incorporating structured attributes. After text preprocessing, bag-of-words features, structured text features, and structured clinical features were extracted from the reports. Next, resampling techniques were incorporated into the training process. Machine learning algorithms were used to build classification models. IRC systems were trained, validated, and tested using a repeated and randomly stratified shuffle-split cross-validation method. Finally, we evaluated the system performance using the F1-measure, precision, and recall over 15 stratified test sets.ResultsThe experimental results demonstrated that the classification system setting considering both data imbalance issues and structured features outperformed the other system settings (with a mean macro-averaged F1-measure of 0.733). Considering the structured features and resampling techniques, this classification system setting significantly improved the mean F1-measure for the rare class by 30.88% (P value < .001) and the mean macro-averaged F1-measure by 8.26% from the baseline system setting (P value < .001). In general, the classification system employing the random forest algorithm and random oversampling method outperformed the others.ConclusionsStructured features provide essential information for categorizing the fall incident severity level. Resampling methods help rebalance the class distribution of the original incident report data, which improves the performance of machine learning models. The IRC framework presented in this study effectively automates the identification of fall incident reports by the severity level.     

沈阳地区76例非何杰金淋巴瘤的分类及其免疫表型病理分析

目的：探讨我国沈阳地区非何杰金淋巴瘤（NHL）的病理特点及免疫组化表型分析。方法：对76例NHL按照WHO新分类进行病理观察和10种抗体免疫表型测定。结果：B系占78．9％,其中弥漫性大B细胞淋巴瘤（DLBL）最多;其次为MALT型（＋／－单核细胞样B细胞）及淋巴浆细胞性淋巴瘤（LPL）。T系占21．1％,以外周T非特殊型最多。CD79a对B－NHL染色阳性率达100％,未见与T细胞的交叉反应。CD3对T－NHL染色阳性率为88％,与B细胞仅有5％的交叉反应。结论：我国沈阳地区NHL以DLBL和外周T非特殊型为最多见,结内单核B和淋巴浆细胞性比例较高。CD79a和CD3分别是具有高度敏感性和特异性的B、T细胞标志抗体。