Multi-stage cascade GAN for synthesis of contrast enhancement CT aorta images from non-contrast CT.

Recently in diagnosis of Aortic dissection (AD), the synthesis of contrast enhanced CT (CE-CT) images from non-contrast CT (NC-CT) images is an important topic. Existing methods have achieved some results but are unable to synthesize a continuous and clear intimal flap on NC-CT images. In this paper, we propose a multi-stage cascade generative adversarial network (MCGAN) to explicitly capture the features of the intimal flap for a better synthesis of aortic dissection images.

Deep learning detects subtle facial expressions in a multilevel society primate.

Facial expressions in nonhuman primates are complex processes involving psychological, emotional, and physiological factors, and may use subtle signals to communicate significant information. However, uncertainty surrounds the functional significance of subtle facial expressions in animals. Using artificial intelligence (AI), this study found that nonhuman primates exhibit subtle facial expressions that are undetectable by human observers.

A Deep Learning Model for Predicting Molecular Subtype of Breast Cancer by Fusing Multiple Sequences of DCE-MRI From Two Institutes.

Rationale And Objectives: To evaluate the performance of deep learning (DL) in predicting different breast cancer molecular subtypes using DCE-MRI from two institutes.

Materials And Methods: This retrospective study included 366 breast cancer patients from two institutes, divided into training (n = 292), validation (n = 49) and testing (n = 25) sets. We first transformed the public DCE-MRI appearance to ours to alleviate small-data-size and class-imbalance issues.

Deep learning-based remote-photoplethysmography measurement from short-time facial video.

. Efficient non-contact heart rate (HR) measurement from facial video has received much attention in health monitoring. Past methods relied on prior knowledge and an unproven hypothesis to extract remote photoplethysmography (rPPG) signals, e.

Frequency importance analysis for chemical exchange saturation transfer magnetic resonance imaging using permuted random forest.

Chemical exchange saturation transfer magnetic resonance imaging (CEST MRI) is a promising molecular imaging tool that allows sensitive detection of endogenous metabolic changes. However, because the CEST spectrum does not display a clear peak like MR spectroscopy, its signal interpretation is challenging, especially under 3-T field strength or with a large saturation B . Herein, as an alternative to conventional Z-spectral fitting approaches, a permuted random forest (PRF) method is developed to determine featured saturation frequencies for lesion identification, so-called CEST frequency importance analysis.

Guided Filter Network for Semantic Image Segmentation.

The existing publicly available datasets with pixel-level labels contain limited categories, and it is difficult to generalize to the real world containing thousands of categories. In this paper, we propose an approach to generate object masks with detailed pixel-level structures/boundaries automatically to enable semantic image segmentation of thousands of targets in the real world without manually labelling. A Guided Filter Network (GFN) is first developed to learn the segmentation knowledge from an existed dataset, and such GFN then transfers the learned segmentation knowledge to generate initial coarse object masks for the target images.

Deep Multiple Instance Hashing for Fast Multi-Object Image Search.

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately.

Directional Modulation Technique Using a Polarization Sensitive Array for Physical Layer Security Enhancement.

Directional modulation (DM), as an emerging promising physical layer security (PLS) technique at the transmitter side with the help of an antenna array, has developed rapidly over decades. In this study, a DM technique using a polarization sensitive array (PSA) to produce the modulation with different polarization states (PSs) at different directions is investigated. A PSA, as a vector sensor, can be employed for more effective DM for an additional degree of freedom (DOF) provided in the polarization domain.

Directional Modulation-Enhanced Multiple Antenna Arrays for Secure and Precise Wireless Transmission.

Directional modulation (DM) technique has the ability to enhance the physical layer security (PLS) of wireless communications. Conventional DM schemes are usually based on a single antenna array with the basic assumption that eavesdroppers (Eves) and legitimate users (LUs) are in different directions. However, it is possible that Eves are in the same direction as LUs in practical applications.

Security analysis of practical continuous-variable quantum key distribution systems under laser seeding attack.

Here, we investigate the security of the practical one-way CVQKD and CV-MDI-QKD systems under laser seeding attack. In particular, Eve can inject a suitable light into the laser diodes of the light source modules in the two kinds of practical CVQKD systems, which results in the increased intensity of the generated optical signal. The parameter estimation under laser seeding attack shows that the secret key rates of these two schemes may be overestimated, which indicates that this attack can open a security loophole for Eve to successfully obtain information about secret key in these practical CVQKD systems.

A Visual Measurement of Water Content of Crude Oil Based on Image Grayscale Accumulated Value Difference.

In the process of oil exploitation, the water level of an oil well can be predicted and the position of reservoir can be estimated by measuring the water content of crude oil, with reference for the automatic production of high efficiency in the oil field. In this paper, a visual measuring method for water content of crude oil is proposed. The oil and water in crude oil samples were separated by centrifugation, distillation or electric dehydration, and a water-oil layered mixture was formed according to the unequal densities.

Three-way decision based reconstruction frame for fluorescence molecular tomography.

Fluorescence molecular tomography (FMT) has been a promising imaging tool because it allows an accurate localizaton and quantitative analysis of the fluorophore distribution in animals. It, however, is still a challenge since its reconstruction suffers from severe ill-posedness. This paper introduces a reconstruction frame based on three-way decisions (TWD) for the inverse problem of FMT.

An improved collaborative filtering method based on similarity.

The recommender system is widely used in the field of e-commerce and plays an important role in guiding customers to make smart decisions. Although many algorithms are available in the recommender system, collaborative filtering is still one of the most used and successful recommendation technologies. In collaborative filtering, similarity calculation is the main issue.

Adaptive threshold method for recovered images of FMT.

This paper proposes a post-processing strategy for recovered images of fluorescence molecular tomography. A threshold value is adaptively obtained from the recovered images without external interference, which is objective because it is extracted from the reconstructed result. The recovered images from simulation experiments and physical phantom experiments are processed by this threshold method.

Monitoring of continuous-variable quantum key distribution system in real environment.

How to guarantee the practical security of continuous-variable quantum key distribution (CVQKD) system has been an important issue in the quantum cryptography applications. In contrast to the previous practical security strategies, which focus on the intercept-resend attack or the Gaussian attack, we investigate the practical security strategy based on a general attack, i.e.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically).

Reconstruction for Limited-Projection Fluorescence Molecular Tomography Based on a Double-Mesh Strategy.

Limited-projection fluorescence molecular tomography (FMT) has short data acquisition time that allows fast resolving of the three-dimensional visualization of fluorophore within small animal . However, limited-projection FMT reconstruction suffers from severe ill-posedness because only limited projections are used for reconstruction. To alleviate the ill-posedness, a feasible region extraction strategy based on a double mesh is presented for limited-projection FMT.

Computational imaging based on time-correlated single-photon-counting technique at low light level.

Imaging at low light levels has drawn much attention. In this paper, a method is experimentally demonstrated to realize computational imaging under weak illumination conditions. In our experiment, only one single-photon detector was used to capture the photons.

25 MHz clock continuous-variable quantum key distribution system over 50 km fiber channel.

In this paper, a practical continuous-variable quantum key distribution system is developed and it runs in the real-world conditions with 25 MHz clock rate. To reach high-rate, we have employed a homodyne detector with maximal bandwidth to 300 MHz and an optimal high-efficiency error reconciliation algorithm with processing speed up to 25 Mbps. To optimize the stability of the system, several key techniques are developed, which include a novel phase compensation algorithm, a polarization feedback algorithm, and related stability method on the modulators.

High performance frame synchronization for continuous variable quantum key distribution systems.

Considering a practical continuous variable quantum key distribution(CVQKD) system, synchronization is of significant importance as it is hardly possible to extract secret keys from unsynchronized strings. In this paper, we proposed a high performance frame synchronization method for CVQKD systems which is capable to operate under low signal-to-noise(SNR) ratios and is compatible with random phase shift induced by quantum channel. A practical implementation of this method with low complexity is presented and its performance is analysed.

Hierarchical Learning of Tree Classifiers for Large-Scale Plant Species Identification.

In this paper, a hierarchical multi-task structural learning algorithm is developed to support large-scale plant species identification, where a visual tree is constructed for organizing large numbers of plant species in a coarse-to-fine fashion and determining the inter-related learning tasks automatically. For a given parent node on the visual tree, it contains a set of sibling coarse-grained categories of plant species or sibling fine-grained plant species, and a multi-task structural learning algorithm is developed to train their inter-related classifiers jointly for enhancing their discrimination power. The inter-level relationship constraint, e.

Continuous-variable quantum key distribution with 1 Mbps secure key rate.

We report the first continuous-variable quantum key distribution (CVQKD) experiment to enable the creation of 1 Mbps secure key rate over 25 km standard telecom fiber in a coarse wavelength division multiplexers (CWDM) environment. The result is achieved with two major technological advances: the use of a 1 GHz shot-noise-limited homodyne detector and the implementation of a 50 MHz clock system. The excess noise due to noise photons from local oscillator and classical data channels in CWDM is controlled effectively.

Hierarchical classification of large-scale patient records for automatic treatment stratification.

In this paper, a hierarchical learning algorithm is developed for classifying large-scale patient records, e.g., categorizing large-scale patient records into large numbers of known patient categories (i.

Leveraging social supports for improving personal expertise on ACL reconstruction and rehabilitation.

In this paper, a social health support system is developed to assist both ACL (anterior cruciate ligament) patients and clinicians on making better decisions and choices for ACL reconstruction and rehabilitation. By providing a good platform to enable more effective sharing of personal expertise and ACL treatments, our social health support system can allow: (1) ACL patients to identify the best-matching social groups and locate the most suitable expertise for personal health management; and (2) clinicians to easily locate the best-matching ACL patients and learn from well-done treatments, so that they can make better decisions for new ACL patients (who have similar ACL injuries and close social principles with those best-matching ACL patients) and prescribe safer and more effective knee rehabilitation treatments.

Constructing distributed Hippocratic video databases for privacy-preserving online patient training and counseling.

Digital video now plays an important role in supporting more profitable online patient training and counseling, and integration of patient training videos from multiple competitive organizations in the health care network will result in better offerings for patients. However, privacy concerns often prevent multiple competitive organizations from sharing and integrating their patient training videos. In addition, patients with infectious or chronic diseases may not want the online patient training organizations to identify who they are or even which video clips they are interested in.