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Background The increasing availability of genome data motivates massive research studies in personalized treatment and precision medicine. Public cloud services provide a flexible way to mitigate the storage and computation burden in conducting genome-wide association studies (GWAS). However, data privacy has been widely concerned when sharing the sensitive information in a cloud environment. Methods We presented a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. We introduced two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics. Results The proposed framework was evaluated for the task of chi-square statistic computation with two case-control datasets from the 2015 iDASH genome privacy protection challenge. Experimental results show that the performance of FORESEE can be significantly improved through algorithmic optimization and parallel computation. Remarkably, the secure approximation division provides significant performance gain, but without missing any significance SNPs in the chi-square association test using the aforementioned datasets. Conclusions Unlike many existing HME based studies, in which final results need to be computed by the data owner due to the lack of the secure division operation, the proposed FORESEE framework support complete outsourcing to the cloud and output the final encrypted chi-square statistics.

Image reconstruction is fundamental for a wide range of scientific research and engineering applications such as optical imaging, biomedical imaging, and remote sensing. Mathematically, it is an ill-posed inverse problem that restores images from incomplete or perturbed measurements. Traditional methods constrain the inverse problem with analytic sparse or low-rank priors considering the sparsity and reduced dimensionality of image representation in the transformed domains and develop iterative algorithms to achieve image reconstruction. With the rise of deep learning, neural network based priors have been achieved in a data-driven manner to significantly enhance image reconstruction, and have evolved from supervised to unsupervised settings. In this paper, we provide a comprehensive overview of image reconstruction with unsupervised deep learning spanning from denoising to generation in the last decade. We interpret this trend of methods from the perspective of well-developed denoisers and bridge denoising and generative priors with the score function linking the logarithmic priors of ground truth images and their perturbed versions. They simultaneously inherit the theoretically sound property of convergence guarantees from analytic methods and enjoy the potential to be a generalized learning-based solution with reliable reconstruction performance. This paper highlights the developing trends of key methods from denoiser-based denoising priors to diffusion model based generative priors with the evolution of their core ideas and methodology characteristics. We explore existing challenges and future directions on the intersection of signal processing and machine learning for image reconstruction.

Background: Endobronchial ultrasound (EBUS) elastography is a new imaging procedure for describing the elasticity of intrathoracic lesions and providing important additional diagnostic information. Objectives: The aim of this study was to utilize the feasibility of qualitative and quantitative methods to evaluate the ability of EBUS elastography to differentiate between benign and malignant mediastinal and hilar lymph nodes (LNs) during EBUS-guided transbronchial needle aspiration (EBUS-TBNA). Methods: Patients with enlarged intrathoracic LNs required for EBUS-TBNA examination at a clinical center for thoracic medicine from January 2014 to April 2014 were prospectively enrolled. EBUS sonographic characteristics on B-mode, vascular patterns and elastography, EBUS-TBNA procedures, pathological findings, and microbiological results were recorded. Furthermore, elastographic patterns (qualitative method) and the mean gray value inside the region of interest (quantitative method) were analyzed. Both methods were compared with a definitive diagnosis of the involved LNs. Results: Fifty-six patients including 68 LNs (33 benign and 35 malignant nodes) were prospectively enrolled into this study and retrospectively analyzed. Using qualitative and quantitative methods, we were able to differentiate between benign and malignant LNs with high sensitivity, specificity, positive and negative predictive values, and accuracy (85.71, 81.82, 83.33, 84.38, and 83.82% vs. 91.43, 72.73, 78.05, 88.89, and 82.35%, respectively). Conclusions: EBUS elastography is potentially capable of further differentiating between benign and malignant LNs. These proposed qualitative and quantitative methods might be useful tools for describing EBUS elastography during EBUS-TBNA.

Genome data are becoming increasingly important for modern medicine. As the rate of increase in DNA sequencing outstrips the rate of increase in disk storage capacity, the storage and data transferring of large genome data are becoming important concerns for biomedical researchers. We propose a two-pass lossless genome compression algorithm, which highlights the synthesis of complementary contextual models, to improve the compression performance. The proposed framework could handle genome compression with and without reference sequences, and demonstrated performance advantages over best existing algorithms. The method for reference-free compression led to bit rates of 1.720 and 1.838 bits per base for bacteria and yeast, which were approximately 3.7% and 2.6% better than the state-of-the-art algorithms. Regarding performance with reference, we tested on the first Korean personal genome sequence data set, and our proposed method demonstrated a 189-fold compression rate, reducing the raw file size from 2986.8 MB to 15.8 MB at a comparable decompression cost with existing algorithms. DNAcompact is freely available at https://sourceforge.net/projects/dnacompact/for research purpose.

Autofluorescence bronchoscopy shows good sensitivity and poor specificity in detecting dysplasia and cancer of the bronchus. Through quantitative analysis on the target area of autofluorescence bronchoscopy image, determine the optimal identification index and reference value for identifying different types of diseases and explore the value of autofluorescence bronchoscopy in diagnosis of lung cancer. Patients with 1 or more preinvasive bronchial lesions were enrolled and followed up by white-light bronchoscope and autofluorescence bronchoscopy. Color space quantitative image analysis was conducted on the lesion shown in the autofluorescence image using MATLAB image measurement software. A retrospective analysis was conducted on 218 cases with 1208 biopsies. One hundred seventy-three cases were diagnosed as positive, which included 151 true-positive cases and 22 false-positive cases. White-light bronchoscope associated with autofluorescence bronchoscopy was able to differentiate between benign and malignant lesion with a high sensitivity, specificity, positive predictive value, and negative predictive value (92.1%, 59.3%, 87.3%, and 71.1%, respectively). Taking 1.485 as the cutoff value of receiver operating characteristic of red-to-green value to differentiate benign and malignant diseases, the diagnostic sensitivity reached 82.3% and the specificity reached 80.5%. U values could differentiate invasive carcinoma and other groups well. Quantitative image analysis method of autofluorescence bronchoscopy provided effective scientific basis for the diagnosis of lung cancer and precancerous lesions.

Along with the development of modern smart cities, human-centric video analysis has been encountering the challenge of analyzing diverse and complex events in real scenes. A complex event relates to dense crowds, anomalous individuals, or collective behaviors. However, limited by the scale and coverage of existing video datasets, few human analysis approaches have reported their performances on such complex events. To this end, we present a new large-scale dataset with comprehensive annotations, named human-in-events or human-centric video analysis in complex events (HiEve), for the understanding of human motions, poses, and actions in a variety of realistic events, especially in crowd and complex events. It contains a record number of poses (> 1 M), the largest number of action instances (> 56k) under complex events, as well as one of the largest numbers of trajectories lasting for longer time (with an average trajectory length of > 480 frames). Based on its diverse annotation, we present two simple baselines for action recognition and pose estimation, respectively. They leverage cross-label information during training to enhance the feature learning in corresponding visual tasks. Experiments show that they could boost the performance of existing action recognition and pose estimation pipelines. More importantly, they prove the widely ranged annotations in HiEve can improve various video tasks. Furthermore, we conduct extensive experiments to benchmark recent video analysis approaches together with our baseline methods, demonstrating HiEve is a challenging dataset for human-centric video analysis. We expect that the dataset will advance the development of cutting-edge techniques in human-centric analysis and the understanding of complex events. The dataset is available at http://humaninevents.org.

Autofluorescence bronchoscopy ( AFB ) shows good sensitivity in detecting dysplasia and bronchopulmonary cancer. However, the poor specificity of AFB would lead to excessive biopsy. The aim of the study is to establish a more effective quantitative method (optimal identification index and reference value) for characterizing the AFB images within the region of interest and discuss AFB 's significance in the diagnosis of central‐type lung cancer. A total of 218 suspected lung cancer patients were enrolled in this study. A quantitative analysis based on color space (red, green, blue[ RGB ] and HSV system) was conducted and the result was compared with the final diagnosis obtained by the pathology of biopsy. Cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low‐grade preinvasive ( LGD ), high‐grade preinvasive ( HGD ), and invasive cancer. Quantitative analyses in multi‐color spaces for the lesions showed by AFB images were conducted by software MATLAB . Finally, there is statistical significance among the different groups in some parameter in RGB and HSV system. So, both RGB and HSV quantitative analysis of autofluorescence bronchoscopy are useful to define benign and malignant diseases, which can objectively guide the bronchoscopist in selecting sites for biopsy with good pathologic correlation.

Photonic neural networks (PNNs) are remarkable analogue artificial intelligence accelerators that compute using photons instead of electrons at low latency, high energy efficiency and high parallelism; however, the existing training approaches cannot address the extensive accumulation of systematic errors in large-scale PNNs, resulting in a considerable decrease in model performance in physical systems. Here we propose dual adaptive training (DAT), which allows the PNN model to adapt to substantial systematic errors and preserves its performance during deployment. By introducing the systematic error prediction networks with task-similarity joint optimization, DAT achieves high similarity mapping between the PNN numerical models and physical systems, as well as highly accurate gradient calculations during dual backpropagation training. We validated the effectiveness of DAT by using diffractive and interference-based PNNs on image classification tasks. Dual adaptive training successfully trained large-scale PNNs under major systematic errors and achieved high classification accuracies. The numerical and experimental results further demonstrated its superior performance over the state-of-the-art in situ training approaches. Dual adaptive training provides critical support for constructing large-scale PNNs to achieve advanced architectures and can be generalized to other types of artificial intelligence systems with analogue computing errors. Despite their efficiency advantages, the performance of photonic neural networks is hampered by the accumulation of inherent systematic errors. Zheng et al. propose a dual backpropagation training approach, which allows the network to adapt to systematic errors, thus outperforming state-of-the-art in situ training approaches.

The aim of this study is to determine the best reference values of the optimal evaluation indexes that identify different disease types. Disease identification was conducted using the YUV quantitative analysis of autofluorescence bronchoscopy (AFB) images in the target areas. Furthermore, this study discusses the significance of AFB in the diagnosis of the central-type lung cancer. A biopsy was conducted for cases that showed pathologic changes under either autofluorescence or white-light bronchoscopy. Moreover, MATLAB was used to carry out the quantitative analyses of lesion in multi-color spaces from AFB images. The cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive cancer. SPSS 11.5 was used to process the data for statistical analysis. The Y values were different and statistically different between invasive cancer and LGD (P<0.001) and invasive cancer and inflammation