Mitigating Docker Security Issues

It is very easy to run applications in Docker. Docker offers an ecosystem that offers a platform for application packaging, distributing and managing within containers. However, Docker platform is yet not matured. Presently, Docker is less secured as compare to virtual machines (VM) and most of the other cloud technologies. The key of reason of Docker inadequate security protocols is containers sharing of Linux kernel, which can lead to risk of privileged escalations. This research is going to outline some major security vulnerabilities at Docker and counter solutions to neutralize such attacks. There are variety of security attacks like insider and outsider. This research will outline both types of attacks and their mitigations strategies. Taking some precautionary measures can save from huge disasters. This research will also present Docker secure deployment guidelines. These guidelines will suggest different configurations to deploy Docker containers in a more secure way.Comment: 11 page

Automating the human action of first-trimester biometry measurement from real-world freehand ultrasound

Objective: Automated medical image analysis solutions should closely mimic complete human actions to be useful in clinical practice. However, more often an automated image analysis solution represents only part of a human task, which restricts its practical utility. In the case of ultrasound-based fetal biometry, an automated solution should ideally recognize key fetal structures in freehand video guidance, select a standard plane from a video stream and perform biometry. A complete automated solution should automate all three subactions. Methods: In this article, we consider how to automate the complete human action of first-trimester biometry measurement from real-world freehand ultrasound. In the proposed hybrid convolutional neural network (CNN) architecture design, a classification regression-based guidance model detects and tracks fetal anatomical structures (using visual cues) in the ultrasound video. Several high-quality standard planes that contain the mid-sagittal view of the fetus are sampled at multiple time stamps (using a custom-designed confident-frame detector) based on the estimated probability values associated with predicted anatomical structures that define the biometry plane. Automated semantic segmentation is performed on the selected frames to extract fetal anatomical landmarks. A crown–rump length (CRL) estimate is calculated as the mean CRL from these multiple frames. Results: Our fully automated method has a high correlation with clinical expert CRL measurement (Pearson's p = 0.92, R-squared [R2] = 0.84) and a low mean absolute error of 0.834 (weeks) for fetal age estimation on a test data set of 42 videos. Conclusion: A novel algorithm for standard plane detection employs a quality detection mechanism defined by clinical standards, ensuring precise biometric measurements

Automated description and workflow analysis of fetal echocardiography in first-trimester ultrasound video scans

This paper presents a novel, fully-automatic framework for fetal echocardiography analysis of full-length routine firsttrimester fetal ultrasound scan video. In this study, a new deep learning architecture, which considers spatio-temporal information and spatial attention, is designed to temporally partition ultrasound video into semantically meaningful segments. The resulting automated semantic annotation is used to analyse cardiac examination workflow. The proposed 2D+t convolution neural network architecture achieves an A1 accuracy of 96.37%, F1 of 95.61%, and precision of 96.18% with 21.49% fewer parameters than the smallest ResNet-based architecture. Automated deep-learning based semantic annotation of unlabelled video scans (n=250) shows a high correlation with expert cardiac annotations (ρ = 0.96, p = 0.0004), thereby demonstrating the applicability of the proposed annotation model for echocardiography workflow analysis

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever

Efficient breast cancer classification network with dual squeeze and excitation in histopathological images.

Medical image analysis methods for mammograms, ultrasound, and magnetic resonance imaging (MRI) cannot provide the underline features on the cellular level to understand the cancer microenvironment which makes them unsuitable for breast cancer subtype classification study. In this paper, we propose a convolutional neural network (CNN)-based breast cancer classification method for hematoxylin and eosin (H&E) whole slide images (WSIs). The proposed method incorporates fused mobile inverted bottleneck convolutions (FMB-Conv) and mobile inverted bottleneck convolutions (MBConv) with a dual squeeze and excitation (DSE) network to accurately classify breast cancer tissue into binary (benign and malignant) and eight subtypes using histopathology images. For that, a pre-trained EfficientNetV2 network is used as a backbone with a modified DSE block that combines the spatial and channel-wise squeeze and excitation layers to highlight important low-level and high-level abstract features. Our method outperformed ResNet101, InceptionResNetV2, and EfficientNetV2 networks on the publicly available BreakHis dataset for the binary and multi-class breast cancer classification in terms of precision, recall, and F1-score on multiple magnification levels