Deep learning-based hyperspectral image super-resolution (SR) methods have achieved great success recently. However, there are two main problems in the previous works. One is to use the typical ...three-dimensional convolution analysis, resulting in more parameters of the network. The other is not to pay more attention to the mining of hyperspectral image spatial information, when the spectral information can be extracted. To address these issues, in this paper, we propose a mixed convolutional network (MCNet) for hyperspectral image super-resolution. We design a novel mixed convolutional module (MCM) to extract the potential features by 2D/3D convolution instead of one convolution, which enables the network to more mine spatial features of hyperspectral image. To explore the effective features from 2D unit, we design the local feature fusion to adaptively analyze from all the hierarchical features in 2D units. In 3D unit, we employ spatial and spectral separable 3D convolution to extract spatial and spectral information, which reduces unaffordable memory usage and training time. Extensive evaluations and comparisons on three benchmark datasets demonstrate that the proposed approach achieves superior performance in comparison to existing state-of-the-art methods.
Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of ...complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval CI: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%;
= .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%;
= .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license.
See also the editorial by Bae in this issue.
A brain tumor is a distorted tissue wherein cells replicate rapidly and indefinitely, with no control over tumor growth. Deep learning has been argued to have the potential to overcome the challenges ...associated with detecting and intervening in brain tumors. It is well established that the segmentation method can be used to remove abnormal tumor regions from the brain, as this is one of the advanced technological classification and detection tools. In the case of brain tumors, early disease detection can be achieved effectively using reliable advanced A.I. and Neural Network classification algorithms. This study aimed to critically analyze the proposed literature solutions, use the Visual Geometry Group (VGG 16) for discovering brain tumors, implement a convolutional neural network (CNN) model framework, and set parameters to train the model for this challenge. VGG is used as one of the highest-performing CNN models because of its simplicity. Furthermore, the study developed an effective approach to detect brain tumors using MRI to aid in making quick, efficient, and precise decisions. Faster CNN used the VGG 16 architecture as a primary network to generate convolutional feature maps, then classified these to yield tumor region suggestions. The prediction accuracy was used to assess performance. Our suggested methodology was evaluated on a dataset for brain tumor diagnosis using MR images comprising 253 MRI brain images, with 155 showing tumors. Our approach could identify brain tumors in MR images. In the testing data, the algorithm outperformed the current conventional approaches for detecting brain tumors (Precision = 96%, 98.15%, 98.41% and F1-score = 91.78%, 92.6% and 91.29% respectively) and achieved an excellent accuracy of CNN 96%, VGG 16 98.5% and Ensemble Model 98.14%. The study also presents future recommendations regarding the proposed research work.
Orthogonal frequency division multiplexing with index modulation (OFDM-IM) performs transmission by considering two modes over OFDM subcarriers, which are the null and the conventional M-ary signal ...constellation. The spectral efficiency (SE) of the system, however, is limited, since the null mode itself does not carry any information and the number of subcarrier activation patterns increases combinatorially. In this paper, a novel IM scheme, called multiple-mode OFDM-IM (MM-OFDM-IM), is proposed for OFDM systems to improve the SE by conveying information through multiple distinguishable modes and their full permutations. A practical and efficient mode selection strategy, which is constrained on the phase shift keying/quadrature amplitude modulation constellations, is designed. Two efficient detectors that provide different tradeoffs between the error performance and detection complexity are also proposed. The principle of MM-OFDM-IM is further extended to the in-phase and quadrature components of OFDM signals, and the method of generating multiple modes from the M-ary pulse amplitude modulation constellation for this modified scheme is also introduced. Bit error rate (BER) analyses are provided for the proposed schemes. Monte Carlo simulations on BER corroborate the analyses and show that the proposed schemes appear as promising multi-carrier transmission alternatives by outperforming the existing OFDM-IM counterparts.
Abstract
Background
The high cost and insufficient supply of human papillomavirus (HPV) vaccines have slowed the pace of controlling cervical cancer. A phase III clinical trial was conducted to ...evaluate the efficacy, safety, and immunogenicity of a novel Escherichia coli-produced bivalent HPV-16/18 vaccine.
Methods
A multicenter, randomized, double-blind trial started on November 22, 2012 in China. In total, 7372 eligible women aged 18–45 years were age-stratified and randomly assigned to receive three doses of the test or control (hepatitis E) vaccine at months 0, 1, and 6. Co-primary endpoints included high-grade genital lesions and persistent infection (over 6 months) associated with HPV-16/18. The primary analysis was performed on a per-protocol susceptible population of individuals who were negative for relevant HPV type-specific neutralizing antibodies (at day 0) and DNA (at day 0 through month 7) and who received three doses of the vaccine. This report presents data from a prespecified interim analysis used for regulatory submission.
Results
In the per-protocol cohort, the efficacies against high-grade genital lesions and persistent infection were 100.0% (95% confidence interval = 55.6% to 100.0%, 0 of 3306 in the vaccine group vs 10 of 3296 in the control group) and 97.8% (95% confidence interval = 87.1% to 99.9%, 1 of 3240 vs 45 of 3246), respectively. The side effects were mild. No vaccine-related serious adverse events were noted. Robust antibody responses for both types were induced and persisted for at least 42 months.
Conclusions
The E coli-produced HPV-16/18 vaccine is well tolerated and highly efficacious against HPV-16/18–associated high-grade genital lesions and persistent infection in women.
Research summary: We examine the consequences of the formalization of the board leadership structure at IPO for board-level turnover. We introduce the concept of director undervaluation. It indicates ...the degree to which a director's qualifications based on normatively accepted criteria for board leadership are not duly reflected in his/her appointments to the board chair and committee chair positions. We find that the higher the average undervaluation of directors on the board ("board undervaluation"), the greater the turnover levels of undervalued directors. This effect is stronger when board interaction frequency is higher. We contribute to the behavioral perspective on corporate governance by introducing justicebased legitimacy as a key normative institution, and by providing a novel predictor of aggregate turnover of directors (as well as the firm's CEO). Managerial summary: Why do outside directors exit the board? We offer a novel answer to this question in the context of newly public firms. We suggest that when directors are passed over for the board chair and committee chair positions despite having higher qualifications than their peers, they have been "undervalued," and a negative board climate is likely to develop. We find that the higher the average undervaluation of directors on the board, the higher the turnover levels of these undervalued directors. More frequent board meetings exacerbate these turnover levels. Further, these turnover effects are not restricted to undervalued directors—even the CEO is more likely to exit. This study demonstrates the critical importance of developing a legitimate and fair board leadership structure.
Sports image decomposition technology is utilized broadly in different fields, and the technologies of sports image action identification situated on sports picture transformation technologies may be ...suitable. This paper utilizes the Moreau envelope and depth recovery for sports image decomposition strategy, an order of work such as a collection of sports image characteristic distillation, and action identification must be achieved in beginning and started with texture functions as well as various relevant functions. And both algorithms have to be utilized to finish the sports picture-oriented sports action identification technology at the lowest time expense. For the improvement of the latest sports image industry form, which is also strongly developed, the people’s affection for sports image is gaining as powerful and creates an improvement of sports image industry still achieving a lot of advantages. The parameters recognition rate and accuracy are compared with various techniques. The proposed methods are effective to achieve the perfect sports image decomposition.
Cellular vehicle to everything (C-V2X) is a technology to achieve vehicle networking, which can improve traffic efficiency and traffic safety. As a special network, the C-V2X system faces many ...security risks. The vehicle to vehicle (V2V) communication transmits traffic condition data, driving path data, user driving habits data, and so on. It is necessary to ensure the opposite equipment is registered C-V2X equipment (installed in the vehicle), and the data transmitted between the equipment is secure. This paper proposes a V2V identity authentication and key agreement scheme based on identity-based cryptograph (IBC). The C-V2X equipment use its vehicle identification (VID) as its public key. The key management center (KMC) generates a private key for the C-V2X equipment according to its VID. The C-V2X equipment transmit secret data encrypted with the opposite equipment public key to the other equipment, they authenticate each other through a challenge response protocol based on identity-based cryptography, and they negotiate the working key used to encrypt the communication data. The scheme can secure the V2V communication with low computational cost and simple architecture and meet the lightweight and efficient communication requirements of the C-V2X system.
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