Beyond CNNs: Exploiting Further Inherent Symmetries in Medical Image Segmentation.

Automatic tumor or lesion segmentation is a crucial step in medical image analysis for computer-aided diagnosis. Although the existing methods based on convolutional neural networks (CNNs) have achieved the state-of-the-art performance, many challenges still remain in medical tumor segmentation. This is because, although the human visual system can detect symmetries in 2-D images effectively, regular CNNs can only exploit translation invariance, overlooking further inherent symmetries existing in medical images, such as rotations and reflections.

2D medical image segmentation via learning multi-scale contextual dependencies.

Automatic medical image segmentation plays an important role as a diagnostic aid in the identification of diseases and their treatment in clinical settings. Recently proposed methods based on Convolutional Neural Networks (CNNs) have demonstrated their potential in image processing tasks, including some medical image analysis tasks. Those methods can learn various feature representations with numerous weight-shared convolutional kernels, however, the missed diagnosis rate of regions of interest (ROIs) is still high in medical image segmentation.

Tumor attention networks: Better feature selection, better tumor segmentation.

Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce the inter-observer variability in estimating the tumor size and the tumor burden, which could further assist physicians to make better therapeutic choices for hepatic diseases and monitoring treatment. Among current mainstream segmentation approaches, multi-layer and multi-kernel convolutional neural networks (CNNs) have attracted much attention in diverse biomedical/medical image segmentation tasks with remarkable performance. However, an arbitrary stacking of feature maps makes CNNs quite inconsistent in imitating the cognition and the visual attention of human beings for a specific visual task.

CTumorGAN: a unified framework for automatic computed tomography tumor segmentation.

Purpose: Unlike the normal organ segmentation task, automatic tumor segmentation is a more challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution, as well as the diversity and individual characteristics of data acquisition procedures and devices. Consequently, most of the recently proposed methods have become increasingly difficult to be applied on a different tumor dataset with good results, and moreover, some tumor segmentors usually fail to generalize beyond those datasets and modalities used in their original evaluation experiments.

Methods: In order to alleviate some of the problems with the recently proposed methods, we propose a novel unified and end-to-end adversarial learning framework for automatic segmentation of any kinds of tumors from CT scans, called CTumorGAN, consisting of a Generator network and a Discriminator network.

Finger-to-Heart (F2H): Authentication for Wireless Implantable Medical Devices.

Any proposal to provide security for implantable medical devices (IMDs), such as cardiac pacemakers and defibrillators, has to achieve a trade-off between security and accessibility for doctors to gain access to an IMD, especially in an emergency scenario. In this paper, we propose a finger-to-heart (F2H) IMD authentication scheme to address this trade-off between security and accessibility. This scheme utilizes a patient's fingerprint to perform authentication for gaining access to the IMD.

A novel fused convolutional neural network for biomedical image classification.

With the advent of biomedical imaging technology, the number of captured and stored biomedical images is rapidly increasing day by day in hospitals, imaging laboratories and biomedical institutions. Therefore, more robust biomedical image analysis technology is needed to meet the requirement of the diagnosis and classification of various kinds of diseases using biomedical images. However, the current biomedical image classification methods and general non-biomedical image classifiers cannot extract more compact biomedical image features or capture the tiny differences between similar images with different types of diseases from the same category.

A novel biomedical image indexing and retrieval system via deep preference learning.

Background And Objectives: The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images.

Cybersecurity and privacy issues for socially integrated mobile healthcare applications operating in a multi-cloud environment.

Social media has enabled information-sharing across massively large networks of people without spending much financial resources and time that are otherwise required in the print and electronic media. Mobile-based social media applications have overwhelmingly changed the information-sharing perspective. However, with the advent of such applications at an unprecedented scale, the privacy of the information is compromised to a larger extent if breach mitigation is not adequate.

Fast and simple high-capacity quantum cryptography with error detection.

Quantum cryptography is commonly used to generate fresh secure keys with quantum signal transmission for instant use between two parties. However, research shows that the relatively low key generation rate hinders its practical use where a symmetric cryptography component consumes the shared key. That is, the security of the symmetric cryptography demands frequent rate of key updates, which leads to a higher consumption of the internal one-time-pad communication bandwidth, since it requires the length of the key to be as long as that of the secret.

A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images.

Background And Objectives: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset.

Hybrid threshold adaptable quantum secret sharing scheme with reverse Huffman-Fibonacci-tree coding.

With prevalent attacks in communication, sharing a secret between communicating parties is an ongoing challenge. Moreover, it is important to integrate quantum solutions with classical secret sharing schemes with low computational cost for the real world use. This paper proposes a novel hybrid threshold adaptable quantum secret sharing scheme, using an m-bonacci orbital angular momentum (OAM) pump, Lagrange interpolation polynomials, and reverse Huffman-Fibonacci-tree coding.

OWL 2 learn profile: an ontology sublanguage for the learning domain.

Many experimental ontologies have been developed for the learning domain for use at different institutions. These ontologies include different OWL/OWL 2 (Web Ontology Language) constructors. However, it is not clear which OWL 2 constructors are the most appropriate ones for designing ontologies for the learning domain.

Multiple ECG Fiducial Points-Based Random Binary Sequence Generation for Securing Wireless Body Area Networks.

Generating random binary sequences (BSes) is a fundamental requirement in cryptography. A BS is a sequence of N bits, and each bit has a value of 0 or 1. For securing sensors within wireless body area networks (WBANs), electrocardiogram (ECG)-based BS generation methods have been widely investigated in which interpulse intervals (IPIs) from each heartbeat cycle are processed to produce BSes.

Cost-Effective Encryption-Based Autonomous Routing Protocol for Efficient and Secure Wireless Sensor Networks.

The deployment of intelligent remote surveillance systems depends on wireless sensor networks (WSNs) composed of various miniature resource-constrained wireless sensor nodes. The development of routing protocols for WSNs is a major challenge because of their severe resource constraints, ad hoc topology and dynamic nature. Among those proposed routing protocols, the biology-inspired self-organized secure autonomous routing protocol (BIOSARP) involves an artificial immune system (AIS) that requires a certain amount of time to build up knowledge of neighboring nodes.

Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks.

As defined by IEEE 802.15.6 standard, channel sharing is a potential method to coordinate inter-network interference among Medical Body Area Networks (MBANs) that are close to one another.

Analysis of authentication protocols in agent-based systems using labeled tableaux.

The study of multiagent systems (MASs) focuses on systems in which many intelligent agents interact with each other using communication protocols. For example, an authentication protocol is used to verify and authorize agents acting on behalf of users to protect restricted data and information. After authentication, two agents should be entitled to believe that they are communicating with each other and not with intruders.