组织学病理图像在深度学习中染色处理的研究进展

深度学习使辅助诊断的软件能够更积极有效地开发和应用,但是组织病理学图像的颜色变化降低了这些算法的性能。染色归一化可以解决扫描仪效应、不同的染色方法、患者的疾病状态、染色时间等因素产生的图像异质性。虚拟染色可以摆脱载玻片染色,减少载玻片的制备步骤,为临床缩短样本的制备时间,节省大量的成本。在缺乏注释训练数据的情况下,病理图像数据增强可用于创建具有纹理和颜色、样式逼真的人工样本来促进网络训练。本文就组织学病理图像在深度学习病理分析中染色处理的染色归一化、虚拟染色和数据增强等方面展开综述,为组织学病理图像在临床上的应用和研究提供参考。     

基于深度学习的肝脏病理图像中肝脂肪变性分级研究

摘要 目的：设计基于深层神经网络模型用来分析肝脏全景病理切片图像（Whole slide images, WSI）的肝脂肪变性分级方法,以实现对非酒精性脂肪性肝病（Non-alcoholic fatty liver disease, NAFLD）病程的辅助诊断。方法：结合临床诊断,以非酒精性脂肪肝活动度积分（NAFLD activity score, NAS）为评价标准,将肝脂肪变性程度分为无、轻度、中度和重度等四级病程,本研究采用多示例学习的策略构建并训练深度神经网络模型,将训练获得的人工智能模型用来实现计算机自动化诊断肝脏病理切片中肝脂肪变性程度分级。结果：通过使用本研究中的人工智能方法可以在3分钟内对一张WSI进行完整的分析,得到该病患肝脏病理切片中肝脂肪变性分级,训练获得的人工智能模型的AUC为0.97,肝脂肪变性分级的平均准确率为78.18%,macro-F1 score、macro-Precision和macro-Recall分别为79.49、82.03和77.10,其结果展示获得的人工智能模型已满足可辅助临床诊断的水平。结论：本研究基于深度学习技术开发的人工智能方法初步实现快速自动化诊断肝脂肪变性分级,展现了其潜在的临床使用价值。     

妊娠初期妇女阴道微生态研究分析

目的 研究妊娠早期妇女阴道微生态状况,分析不同类型阴道炎在妊娠早期的发病率,比较Nugent评分和Claeys评级两种标准在诊断阴道炎方面的一致性。方法 选择2017年7月至2017年10月在上海市第一妇婴保健院产科门诊就诊的妊娠早期妇女726例（孕周≤12周）,采集阴道分泌物进行外阴阴道假丝酵母菌性阴道炎（vulvovaginal candidiasis,VVC）、滴虫性阴道炎（trichomonas vaginitis,TV）、需氧性阴道炎（aerobic vaginitis,AV）、细菌性阴道病（bacterial vaginosis,BV）检测,AV和（或）VVC阳性的分泌物进行培养鉴定;对革兰染色涂片进行Nugent评分和Claeys评级,以Nugent标准为金标准,评价Claeys评级在诊断阴道炎方面的性能。结果 726例标本中共检出异常标本153例（21.07%）,AV、BV、TV、VVC的发生率分别为5.23%、11.02%、0.13%、11.71%,混合感染占异常标本28.76%（44/153）,最常见的混合感染类型是BV合并VVC,AV最常见的致病菌是无乳链球菌、大肠杆菌、粪肠球菌。以Nugent方法为金标准,Claeys评级的敏感性、特异性、阳性预测值、阴性预测值、Kappa值分别为96.2%、88.1%、85.4%、97.1%、≥0.880。结论 妊娠早期阴道微生态失衡发生率较高且混合感染存在较高的比例,湿片和革兰染色结合分析可以提高阴道炎诊断的准确性。Claeys评级在阴道菌群分析上更全面、更简便,可作为Nugent标准的替代方法用于临床和科研。     

800例妇科门诊妇女阴道感染及微生态状况分析

目的了解普通妇科门诊妇女阴道分泌物病原体感染及阴道微生态状况。方法用革兰染色法检查普通妇科门诊妇女阴道分泌物,并进行Nugent评分。结果800例普通妇科门诊妇女阴道分泌物中,假丝酵母菌阳性236例,阳性率29．5％;阴道毛滴虫阳性21例,阳性率2．63％;Nugent评分：正常（0-3分）248例（31．0％）,临界（4-6分）283例（35．4％）,细菌性阴道病（BV）（7-10分）269例（33．6％）。结论妇科门诊妇女对各种病原微生物抵抗力不足,阴道微生态状况有待改善。     

眼底图像深度学习技术对屈光不正的智能诊断研究

目的:提出一种基于人类视觉注意力机制的RE-Net网络结构以使卷积神经网络(CNN)更适用于眼底相的屈光不正的智能诊断评估。方法:RE-Net由Res Net34作为骨干网络,进一步使用了上下文注意力模块,包括通道注意力机制和空间注意力机制,使其相应的通道发挥最大的作用,提高响应区域的权重。结果:使用了4358张眼底图像作为RE-Net的训练集。在包含485张眼底图像的测试集上,分类准确率分别为,高度近视93.3%,中度近视89.7%,轻度近视83.2%,轻度远视82.5%,中度远视79.5%,重度远视84.6%,平均分类准确率达85.5%,曲线下面积(AUC)为0.909,灵敏度为0.93,特异性为0.89, Kappa值为0.79 (x~2=23.21,P0.05)。结论:基于深度学习的RE-NET人工智能诊断系统能较好进行屈光不正的诊断评估,有望为屈光不正提供一种新的筛查工具。     

应用深度学习分割光学显微镜下的细胞图像

目的 为了在细胞培养过程中便捷地分析细胞的数目和形态.方法 本文将深度学习应用到细胞识别中,实现了一种可以通过普通光学显微镜拍照,并直接在培养皿中进行细胞识别计数的方法.结果 通过构建U-Net网络结构,并对贴壁细胞和悬浮细胞图像进行标记训练,来实现贴壁细胞和悬浮细胞的分割计数.同时,用该算法绘制细胞生长曲线以及计算抑...     

基于联邦深度学习的皮肤病智能诊断研究

近年来,基于人工智能的皮肤病智能诊断已经成为智慧医疗领域的热门课题。然而由于单一机构的数据有限,局部训练的神经网络难以满足医疗诊断服务的性能需求,从分散机构中收集数据的集中式学习又存在隐私泄漏的风险。基于上述挑战,本文提出一种基于联邦深度学习的皮肤病智能诊断算法。具体地,对比主流的集中式学习,为了在整合多方数据时防止隐私泄漏,本文引入了联邦学习。各客户端将本地模型发送到中心服务器进行聚合,中心服务器再将聚合得到的全局模型同步到各客户端,实现神经网络模型的训练。进一步,为了解决联邦学习中数据异构性的问题,本文在交叉熵损失的基础上增加了修正项,通过修正项限制本地模型和全局模型间的差异,增加模型对异构数据的关注度,从而减小数据异构对诊断结果的影响。实验结果表明,本文所提的皮肤病智能诊断算法与现有相关方案相比,诊断准确度提高了3%~4%,达到75.9%。     

基于深度卷积神经网络的树轮宽度测量算法

数字图像处理技术已被广泛用于树轮宽度测量,但大多集中在边界清晰可见的针叶树种,对于木材解剖结构复杂、树轮边界清晰度较差的阔叶树种,传统的图像处理技术表现不佳。为了改善阔叶树种的树轮边界识别精度,本文提出了一种基于U-Net卷积神经网络模型的树轮宽度测量算法。以鱼鳞云杉(Picea jezoensis var.komarovii)、臭冷杉(Abies nephrolepis)、红松(Pinus koraiensis)、白桦(Betula platyphylla)、枫桦(Betula costata)、榆树(Ulmus pumila)的树芯为对象,提出了一种基于U-Net的树轮边界检测模型。采用3种评价指标比较了U-Net方法与手工标注方法的差异,并与WinDENDRO测量得到的树轮宽度进行了精度对比。结果显示,U-Net识别到的树轮边界与实际边界精确匹配,尤其是对阔叶树种树轮边界的检测精度相比传统的数字图像处理方法有显著提高,通过3种评价指标证明所得到的树轮边界精确可靠,在树轮分析中具有较高的实用价值。     

55例早期食管癌浸润深度的临床病理因素分析

目的:探讨早期食管癌的浸润深度的临床病理因素。方法:对术前胃镜发现、术后病理证实的55例早期食管癌进行统计,分析早期食管癌内镜下表现、肿瘤分化程度、淋巴转移与浸润深度之间的关系。结果:表面扩散生长型、腔内生长型高度<5mm、双向生长型高度<2mm及壁内生长型深度<0.5mm提示黏膜内癌。腔内生长型高度≥5mm、壁内生长型高度≥0.5mm、双向生长型高度≥2mm及混合生长型提示黏膜下癌。肿瘤分化程度与浸润深度呈负相关。4例淋巴结转移及2例淋巴管癌栓,其中1例为黏膜癌。5例为黏膜下层癌,原位癌均未发生转移。结论:内镜下表现与浸润深度有密切关系,肿瘤恶性程度越高则越易向深层浸润,浸润越深淋巴结转移率越高。     

基于深度学习的作物基因组学和遗传改良

随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。     

基于深度学习的微藻自动检测系统研究

目的 微藻养殖产业规模巨大,在养殖过程中微藻易受杂菌和其他污染物的影响,因此需要定期对微藻进行检测,以确定其生长情况。现有的光学显微成像法和光谱分析法对实验人员、实验设备及场地的要求较高,无法做到实时快速检测。为了实现实时快速检测,需要一套检测要求低、速度快的实时微藻检测系统。方法 本文开发了一种基于深度学习的微藻检测系统,通过搭建一套基于明场成像的显微成像设备,使用采集的图像训练基于YOLOv3的神经网络,并将训练好的神经网络部署到微型计算机,从而实现了实时便携微藻检测。本文对特征提取网络进行改进,包括引入跨区域残差连接机制和注意力选择机制,另外还将优化器改为Adam优化器,使用多阶段多方法组合策略。结果 加载跨区域残差连接机制时最高平均精度（mAP）值为0.92。通过与人工结果进行对比,得到检测误差为2.47%。结论 该系统能够实现微藻实时便携检测,提供较为准确的检测结果,可以应用于微藻养殖中的定期检测。     

Deep self-supervised transformation learning for leukocyte classification

The scarcity of training annotation is one of the major challenges for the application of deep learning technology in medical image analysis. Recently, self-supervised learning provides a powerful solution to alleviate this challenge by extracting useful features from a large number of unlabeled training data. In this article, we propose a simple and effective self-supervised learning method for leukocyte classification by identifying the different transformations of leukocyte images, without requiring a large batch of negative sampling or specialized architectures. Specifically, a convolutional neural network backbone takes different transformations of leukocyte image as input for feature extraction. Then, a pretext task of self-supervised transformation recognition on the extracted feature is conducted by a classifier, which helps the backbone learn useful representations that generalize well across different leukocyte types and datasets. In the experiment, we systematically study the effect of different transformation compositions on useful leukocyte feature extraction. Compared with five typical baselines of self-supervised image classification, experimental results demonstrate that our method performs better in different evaluation protocols including linear evaluation, domain transfer, and finetuning, which proves the effectiveness of the proposed method.     

Lung Nodule Detection Based on YOLOv3 Deep Learning with Limited Datasets

The early symptom of lung tumor is always appeared as nodule on CT scans, among which 30% to 40% are malignant according to statistics studies. Therefore, early detection and classification of lung nodules are crucial to the treatment of lung cancer. With the increasing prevalence of lung cancer, large amount of CT images waiting for diagnosis are huge burdens to doctors who may missed or false detect abnormalities due to fatigue. Methods: In this study, we propose a novel lung nodule detection method based on YOLOv3 deep learning algorithm with only one preprocessing step is needed. In order to overcome the problem of less training data when starting a new study of Computer Aided Diagnosis (CAD), we firstly pick up a small number of diseased regions to simulate a limited datasets training procedure: 5 nodule patterns are selected and deformed into 110 nodules by random geometric transformation before fusing into 10 normal lung CT images using Poisson image editing. According to the experimental results, the Poisson fusion method achieves a detection rate of about 65.24% for testing 100 new images. Secondly, 419 slices from common database RIDER are used to train and test our YOLOv3 network. The time of lung nodule detection by YOLOv3 is shortened by 2–3 times compared with the mainstream algorithm, with the detection accuracy rate of 95.17%. Finally, the configuration of YOLOv3 is optimized by the learning data sets. The results show that YOLOv3 has the advantages of high speed and high accuracy in lung nodule detection, and it can access a large amount of CT image data within a short time to meet the huge demand of clinical practice. In addition, the use of Poisson image editing algorithms to generate data sets can reduce the need for raw training data and improve the training efficiency.     

深度学习在药物活性预测研究中的应用

药物从研发到临床应用需要耗费较长的时间，研发期间的投入成本可高达十几亿元。而随着医药研发与人工智能的结合以及生物信息学的飞速发展，药物活性相关数据急剧增加，传统的实验手段进行药物活性预测已经难以满足药物研发的需求。借助算法来辅助药物研发，解决药物研发中的各种问题能够大大推动药物研发进程。传统机器学习方法尤其是随机森林、支持向量机和人工神经网络在药物活性方面能够达到较高的预测精度。深度学习由于具有多层神经网络，模型可以接收高维的输入变量且不需要人工限定数据输入特征，可以拟合较为复杂的函数模型，应用于药物研发可以进一步提高各个环节的效率。在药物活性预测中应用较为广泛的深度学习模型主要是深度神经网络（deep neural networks，DNN）、循环神经网络（recurrent neural networks，RNN）和自编码器（auto encoder，AE），而生成对抗网络（generative adversarial networks，GAN）由于其生成数据的能力常常被用来和其他模型结合进行数据增强。近年来深度学习在药物分子活性预测方面的研究和应用综述表明，深度学习模型的准确度和效率均高于传统实验方法和传统机器学习方法。因此，深度学习模型有望成为药物研发领域未来十年最重要的辅助计算模型。     

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.     

Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.     

BrcaSeg: A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression. Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment. Here, we propose BrcaSeg, an image analysis pipeline based on a convolutional neural network (CNN) model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin (H&E) stained histopathological images. The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas (TCGA) Program. BrcaSeg achieves a classification accuracy of 91.02%, which outperforms other state-of-the-art methods. Using this model, we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data. We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios. Gene Ontology (GO) enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes, whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues. Taken all together, our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors. BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.