妇幼影像学检查技术的新进展

乳腺癌是女性肿瘤中发病率最高的一种,我国每年有4万人死于乳腺癌,发病率与5年前相比上升了3倍多。乳腺X线摄影是乳腺病变首选的影像检查手段,数字化摄影的迅速发展更明显提高肿块的捡出率。超声波检测可成为乳腺X线摄影的有力补充,CT和MR对于乳腺病变检查也各有其独特的效果,本文对此作一扼要归纳,同时也对各种影像学检查技术在儿科方面的应用新进展进行简明概述。     

Meta-heuristics for Feature Selection and Classification in Diagnostic BreastCancer

One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set of deep neural networks (VGGNet, ResNet-50, AlexNet, and GoogLeNet) are employed. The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy. The selected features are used to train a neural network to finally classify the thermal images of breast cancer. To achieve accurate classification, the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm. Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature. Moreover, several experiments were conducted to compare the performance of the proposed approach with the other approaches. The results of these experiments emphasized the superiority of the proposed approach.     

Automatic cervical cancer segmentation in multimodal magnetic resonance imaging using an EfficientNet encoder in UNet++ architecture

Automatic cervical cancer segmentation in multimodal magnetic resonance imaging (MRI) is essential because tumor location and delineation can support patients' diagnosis and treatment planning. To meet this clinical demand, we present an encoder–decoder deep learning architecture which employs an EfficientNet encoder in the UNet++ architecture (E-UNet++). EfficientNet helps in effectively encoding multiscale image features. The nested decoders with skip connections aggregate multiscale features from low-level to high-level, which helps in detecting fine-grained details. A cohort of 228 cervical cancer patients with multimodal MRI sequences, including T2-weighted imaging, diffusion-weighted imaging, apparent diffusion coefficient imaging, contrast enhancement T1-weighted imaging, and dynamic contrast-enhanced imaging (DCE), has been explored. Evaluations are performed by considering either single or multimodal MRI with standard segmentation quantitative metrics: dice similarity coefficient (DSC), intersection over union (IOU), and 95% Hausdorff distance (HD). Our results show that the E-UNet++ model can achieve DSC values of 0.681–0.786, IOU values of 0.558–0.678, and 95% HD values of 3.779–7.411 pixels in different single sequences. Meanwhile, it provides DSC values of 0.644 and 0.687 on three DCE subsequences and all MRI sequences together. Our designed model is superior to other comparative models, which shows the potential to be used as an artificial intelligence tool for cervical cancer segmentation in multimodal MRI.     

苹果霉心病无损检测研究进展

目的介绍几种常用的苹果霉心病无损检测技术及建模方法,为苹果及其他水果病害的无损检测提供新的思路和依据。方法综述苹果霉心病无损检测技术和建模方法的原理,以及国内外的研究进展。结果在苹果霉心病的无损检测中,光谱检测技术应用较广泛,生物电阻抗特性、各种成像检测技术和机器智能感官仿生检测技术逐渐得到发展,融合多源信息的霉心病检测技术也越来越受到研究者的青睐。结论苹果霉心病无损检测的研究还处于初级阶段,许多检测技术和建模方法的参数还需要进一步优化。随着研究的深入,无损检测技术不仅可以应用于水果病害的检测,还可以应用于其他食品工业,具有广阔的研究前景。     

Aptamer-conjugated nanoparticles for the collection and detection of multiple cancer cells

We have extended the use the aptamer-conjugated nanoparticles for the collection and detection of multiple cancer cells. The aptamers were selected using a cell-based SELEX strategy in our laboratory for cancer cells that, when utilized in this method, allow for the selective recognition of the cells from complex mixtures including fetal bovine serum samples. Aptamer-conjugated magnetic nanoparticles were used for the selective targeting cell extraction, and aptamer-conjugated fluorescent nanoparticles were employed for sensitive cellular detection. Employing both types of nanoparticles allows for selective and sensitive detection not possible by using the particles separately. Fluorescent nanoparticles amplify the signal intensity versus a single fluorophore label resulting in improved sensitivity. In addition, aptamer-conjugated magnetic nanoparticles allow for extraction and enrichment of target cells not possible with other separation methods. Fluorescent imaging and a microplate reader were used for cellular detection to demonstrate the wide applicability of this methodology for medical diagnostics and cell enrichment and separation.     

Performance of near-infrared dyes as effective contrast agents for breast cancer detection through simulation of photoacoustic imaging

ABSTRACT

Targeted photoacoustic imaging using exogenous contrast agents can potentially improve early detection of breast cancer, even at significant depths inside the breast. In this study, computer simulations were performed to compare the photoacoustic performance of 11 different near-infrared (NIR) dyes for detecting tumours deep inside the breast tissue. It was observed that the three high performing NIR dyes produced at least two-fold contrast enhancement of a spherical breast tumour embedded at 4?cm depth inside the breast than those of the corresponding endogenous contrast agents. These three selected dyes were employed to visualize small blood vessels deep inside the breast tissue. Although methylene blue provided the best contrast in visualizing tumour blood vessels at depths beyond 3?cm, considering other factors such as availability of suitable targeting agent, indocyanine green at 800?nm may be preferred over all other dyes for deep breast imaging applications.     

图像处理与识别在果蔬成熟度监测中的研究及应用

目的 分析了果蔬成熟度自动监测对发展智慧农业的重要意义,对图像处理与识别技术在监测果蔬成熟度领域的研究与应用现状进行综述、总结与展望,以期为我国发展果蔬成熟度在线或自动检测识别技术提供参考。方法 对图像处理与识别在监测果蔬成熟度中的原理、优势进行分析,对特征提取、深度学习中的神经网络在该领域中的应用研究进展进行综述。结果 采用以图像处理和识别为核心的计算机视觉检测技术对果蔬的颜色、纹理等外部特征进行成熟度检测具有优势,结合神经网络对果蔬成熟度进行检测的识别率高,可在采摘、运输等场景对果蔬成熟度进行监测。结论 图像处理与识别技术在果蔬成熟度监测领域有望得到突破,将催生更多新的应用场景。     

Online variational learning of finite inverted Beta-Liouville mixture model for biomedical analysis

Image segmentation is widely applied for biomedical image analysis. However, segmentation of medical images is challenging due to many image modalities, such as, CT, X-ray, MRI, microscopy among others. An additional challenge to this is the high variability, inconsistent regions with missing edges, absence of texture contrast, and high noise in the background of biomedical images. Thus, many segmentation approaches have been investigated to address these issues and to transform medical images into meaningful information. During the past decade, finite mixture models have been revealed to be one of the most flexible and popular approaches in data clustering. In this article, we propose a statistical framework for online variational learning of finite inverted Beta-Liouville mixture model for clustering medical images. The online variational learning framework is used to estimate the parameters and the number of mixture components simultaneously, thus decreasing the computational complexity of the model. To this end, we evaluated our proposed algorithm on five different biomedical image data sets including optic disc detection and localization in diabetic retinopathy, digital imaging in melanoma lesion detection and segmentation, brain tumor detection, colon cancer detection and computer aid detection (CAD) of Malaria. Furthermore, we compared the proposed algorithm with three other popular algorithms. In our results, we analyze that the proposed online variational learning of finite IBL mixture model algorithm performs accurately on multiple modalities of medical images. It detects the disease patterns with high confidence. Computational and statistical approaches like the one presented in this article hold a significant impact on medical image analysis and interpretation in both clinical applications and scientific research. We believe that the proposed algorithm has the capacity to address multi modal biomedical image data sets and can be further applied by researchers to analyze correct disease patterns.     

Optical imaging-guided cancer therapy with fluorescent nanoparticles

The diagnosis and treatment of cancer have been greatly improved with the recent developments in nanotechnology. One of the promising nanoscale tools for cancer diagnosis is fluorescent nanoparticles (NPs), such as organic dye-doped NPs, quantum dots and upconversion NPs that enable highly sensitive optical imaging of cancer at cellular and animal level. Furthermore, the emerging development of novel multi-functional NPs, which can be conjugated with several functional molecules simultaneously including targeting moieties, therapeutic agents and imaging probes, provides new potentials for clinical therapies and diagnostics and undoubtedly will play a critical role in cancer therapy. In this article, we review the types and characteristics of fluorescent NPs, in vitro and in vivo imaging of cancer using fluorescent NPs and multi-functional NPs for imaging-guided cancer therapy.     

基于StyleGAN的草图生成产品设计效果图方法研究

目的 解决当前产品设计表达中存在对设计师要求高、设计思维具有局限性、设计周期长等问题。方法 提出基于StyleGAN的草图快速生成产品效果图像的方法,该方法利用图像变形技术,将不同程度的产品草图生成真实产品效果图像。结果 该方法可有效地满足设计师创作需求,也能为没有绘画基础的用户生成高质量的产品设计方案。结论 将基于深度学习的StyleGAN模型应用于草图生成真实产品效果图像中,能快速、高效地完成产品设计表达过程,为产品设计表达提供了一个基于深度学习技术的参考框架,也是传统产品设计在人工智能时代的一次创新性探索。     

新技术在堤坝隐患自动识别系统中的应用

目的　为了对堤坝隐患进行有效的识别 ,本文提出了 3种新技术 ,即堤防隐患雷达探测系统、多传感器探测网络系统及其数据融合技术、红外成像技术在堤坝隐患自动识别系统中的应用 .方法　介绍了这些应用的系统组成、原理以及方案 .结果与结论　通过研究发现这 3种新技术能够用于堤坝隐患自动识别系统中 ,证明了这些方法的有效性 .     

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019 (COVID-19). The usage of sophisticated artificial intelligence technology (AI) and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages. In this research, the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia, reported COVID-19 disease, and normal cases. The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures. Transfer Learning technique has been implemented in this work. Transfer learning is an ambitious task, but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images. The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection. Since all diagnostic measures show failure levels that pose questions, the scientific profession should determine the probability of integration of X-rays with the clinical treatment, utilizing the results. The proposed model achieved 96.73% accuracy outperforming the ResNet50 and traditional Resnet18 models. Based on our findings, the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.     

Deep Learning-Based Intrusion System for Vehicular Ad Hoc Networks

The increasing use of the Internet with vehicles has made travel more convenient. However, hackers can attack intelligent vehicles through various technical loopholes, resulting in a range of security issues. Due to these security issues, the safety protection technology of the in-vehicle system has become a focus of research. Using the advanced autoencoder network and recurrent neural network in deep learning, we investigated the intrusion detection system based on the in-vehicle system. We combined two algorithms to realize the efficient learning of the vehicle’s boundary behavior and the detection of intrusive behavior. In order to verify the accuracy and efficiency of the proposed model, it was evaluated using real vehicle data. The experimental results show that the combination of the two technologies can effectively and accurately identify abnormal boundary behavior. The parameters of the model are self-iteratively updated using the time-based back propagation algorithm. We verified that the model proposed in this study can reach a nearly 96% accurate detection rate.     

Automated Deep Learning Empowered Breast Cancer Diagnosis Using Biomedical Mammogram Images

Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer diagnosis (ADL-BCD) model using digital mammograms. The goal of the ADL-BCD technique is to properly detect the existence of breast lesions using digital mammograms. The proposed model involves Gaussian filter based pre-processing and Tsallis entropy based image segmentation. In addition, Deep Convolutional Neural Network based Residual Network (ResNet 34) is applied for feature extraction purposes. Specifically, a hyper parameter tuning process using chimp optimization algorithm (COA) is applied to tune the parameters involved in ResNet 34 model. The wavelet neural network (WNN) is used for the classification of digital mammograms for the detection of breast cancer. The ADL-BCD method is evaluated using a benchmark dataset and the results are analyzed under several performance measures. The simulation outcome indicated that the ADL-BCD model outperforms the state of art methods in terms of different measures.     

基于YOLOv5和U-NET的多目标药盒抓取系统设计

目的 解决传统机器视觉机器人抓取系统对多目标及复杂目标背景分割不精确导致的目标定位精度差而影响机器人抓取效率的问题,提出一种新的深度学习抓取识别定位系统。方法 搭建由Delta机械臂、PC上位机、双目相机等组成的硬件系统,对工业部署常用的YOLO系列算法进行对比研究。将YOLO与U-NET相结合,用于目标的检测和分割。在精确分割出属于目标和背景目标的像素区域的同时,计算边缘和中心位置信息,运用立体视觉技术得到三维位置,并转换为世界坐标系,由PC机引导机械臂去完成抓取任务。结果 深度学习目标检测和图像分割相结合的系统在较复杂背景、多目标的场景下比未添加图像分割的算法拥有更好的目标定位精确度。结论 YOLOv5和U-NET相结合的目标定位抓取方法具有较高的鲁棒性,达到了并联机械臂的抓取要求。该方法能够运用于其他多自由度机械臂上,具有良好的应用价值。     

Breast cancer detection by mapping hemoglobin concentration and oxygen saturation

Near-infrared (NIR) spectroscopic imaging technology provides a new modality for measuring changes in total hemoglobin concentration (HbT) and blood oxygen saturation (SO2) in human tissue. The technology can be used to detect breast cancer because cancers may cause greater vascularization and greater oxygen consumption than in normal tissue. Based on the NIR technology, ViOptix, Inc., has developed an optical device that provides two-dimensional mapping of HbT and SO2 in human tissue. As an adjunctive tool to mammography, the device was preliminarily tested in a clinical trial with 50 mammogram-positive patients at the Massachusetts General Hospital. The results of the clinical trial demonstrate that the device can reach as much as 92% diagnostic sensitivity and 67% specificity in detecting ductal carcinoma. These results may indicate that the NIR technology can potentially be used as an adjunct to mammography for breast cancer detection to reduce the number of biopsies performed.     

Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection

This proposal aims to enhance the accuracy of a dermoscopic skin cancer diagnosis with the aid of novel deep learning architecture. The proposed skin cancer detection model involves four main steps: (a) preprocessing, (b) segmentation, (c) feature extraction, and (d) classification. The dermoscopic images initially subjected to a preprocessing step that includes image enhancement and hair removal. After preprocessing, the segmentation of lesion is deployed by an optimized region growing algorithm. In the feature extraction phase, local features, color morphology features, and morphological transformation-based features are extracted. Moreover, the classification phase uses a modified deep learning algorithm by merging the optimization concept into recurrent neural network (RNN). As the main contribution, the region growing and RNN improved by the modified deer hunting optimization algorithm (DHOA) termed as Fitness Adaptive DHOA (FA-DHOA). Finally, the analysis has been performed to verify the effectiveness of the proposed method.     

爆炸物的探测技术综述

介绍了目前已经应用的和在研的各类爆炸物探测方法,并指出了每种方法的优缺点。其中,重点介绍了X射线成像技术及中子探测技术。目前,许多新的检测技术仍处在不成熟的阶段,漏报率与误报率始终存在,并且对于核检测技术而言,高灵敏度对应的高能量源与相应的屏蔽措施造成的设备体积过大是一对需要解决的矛盾。