Independent vector analysis based on overlapped cliques of variable width for frequency-domain blind signal separation

A novel method is proposed to improve the performance of independent vector analysis (IVA) for blind signal separation of acoustic mixtures. IVA is a frequency-domain approach that successfully resolves the well-known permutation problem by applying a spherical dependency model to all pairs of frequency bins. The dependency model of IVA is equivalent to a single clique in an undirected graph; a clique in graph theory is defined as a subset of vertices in which any pair of vertices is connected by an undirected edge. Therefore, IVA imposes the same amount of statistical dependency on every pair of frequency bins, which may not match the characteristics of real-world signals. The proposed method allows variable amounts of statistical dependencies according to the correlation coefficients observed in real acoustic signals and, hence, enables more accurate modeling of statistical dependencies. A number of cliques constitutes the new dependency graph so that neighboring frequency bins are assigned to the same clique, while distant bins are assigned to different cliques. The permutation ambiguity is resolved by overlapped frequency bins between neighboring cliques. For speech signals, we observed especially strong correlations across neighboring frequency bins and a decrease in these correlations with an increase in the distance between bins. The clique sizes are either fixed, or determined by the reciprocal of the mel-frequency scale to impose a wider dependency on low-frequency components. Experimental results showed improved performances over conventional IVA. The signal-to-interference ratio improved from 15.5 to 18.8 dB on average for seven different source locations. When we varied the clique sizes according to the observed correlations, the stability of the proposed method increased with a large number of cliques.open4

Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Secure eHealth-Care Service on Self-Organizing Software Platform

There are several applications connected to IT health devices on the self-organizing software platform (SoSp) that allow patients or elderly users to be cared for remotely by their family doctors under normal circumstances or during emergencies. An evaluation of the SoSp applied through PAAR watch/self-organizing software platform router was conducted targeting a simple user interface for aging users, without the existence of extrasettings based on patient movement. On the other hand, like normal medical records, the access to, and transmission of, health information via PAAR watch/self-organizing software platform requires privacy protection. This paper proposes a security framework for health information management of the SoSp. The proposed framework was designed to ensure easy detection of identification information for typical users. In addition, it provides powerful protection of the user’s health information

Candidate selection based on significance testing and its use in normalisation and scoring

ABSTRACT Log likelihood ratio normalisation and scoring methods have been studied by many researchers and have improved the performance of speaker identi cation systems. However, these studies have disadvantages: the recognised distorted speech segments are di erent for each speaker. Also the background model in log likelihood ratio normalisation is changed in each speech segment e v en for the same speaker. This paper presents two techniques. Firstly, candidate selection based on signi cance testing, which designs the background speaker model more accurately. And secondly, the scoring method, which recognises the same distorted speech segments for every speaker. We perform a n umber of experiments with the SPIDRE database

Encoder-Weighted W-Net for Unsupervised Segmentation of Cervix Region in Colposcopy Images

Simple Summary The cervix region segmentation significantly affects the accuracy of diagnosis when analyzing colposcopy. Detecting the cervix region requires manual, intensive, and time-consuming labor from a trained gynecologist. In this paper, we propose a deep learning-based automatic cervix region segmentation method that enables the extraction of the region of interest from colposcopy images in an unsupervised manner. The segmentation performance with a Dice coefficient improved from 0.612 to 0.710 by applying the proposed loss function and encoder-weighted learning scheme and showed the best performance among all the compared methods. The automatically detected cervix region can improve the performance of image-based interpretation and diagnosis by suggesting where the clinicians should focus during colposcopy analysis. Cervical cancer can be prevented and treated better if it is diagnosed early. Colposcopy, a way of clinically looking at the cervix region, is an efficient method for cervical cancer screening and its early detection. The cervix region segmentation significantly affects the performance of computer-aided diagnostics using a colposcopy, particularly cervical intraepithelial neoplasia (CIN) classification. However, there are few studies of cervix segmentation in colposcopy, and no studies of fully unsupervised cervix region detection without image pre- and post-processing. In this study, we propose a deep learning-based unsupervised method to identify cervix regions without pre- and post-processing. A new loss function and a novel scheduling scheme for the baseline W-Net are proposed for fully unsupervised cervix region segmentation in colposcopy. The experimental results showed that the proposed method achieved the best performance in the cervix segmentation with a Dice coefficient of 0.71 with less computational cost. The proposed method produced cervix segmentation masks with more reduction in outliers and can be applied before CIN detection or other diagnoses to improve diagnostic performance. Our results demonstrate that the proposed method not only assists medical specialists in diagnosis in practical situations but also shows the potential of an unsupervised segmentation approach in colposcopy

Type and cause of liver disease in Korea: single-center experience, 2005-2010

Background/AimsThe aim of this study was to describe the types and causes of liver disease in patients from a single community hospital in Korea between April 2005 and May 2010.MethodsA cohort of patients who visited the liver clinic of the hospital during the aforementioned time period were consecutively enrolled (n=6,307). Consistent diagnostic criteria for each liver disease were set by a single, experienced hepatologist, and the diagnosis of all of the enrolled patients was confirmed by retrospective review of their medical records.ResultsAmong the 6,307 patients, 528 (8.4%) were classified as acute hepatitis, 3,957 (62.7%) as chronic hepatitis, 767 (12.2%) as liver cirrhosis, 509 (8.1%) as primary liver cancer, and 546 (8.7%) as a benign liver mass or other diseases. The etiologies in the acute hepatitis group in decreasing order of prevalence were hepatitis A (44.3%), toxic hepatitis (32.4%), other hepatitis viruses (13.8%), and cryptogenic hepatitis (9.1%). In the chronic hepatitis group, 51.2% of cases were attributed to viral hepatitis, 33.3% to nonalcoholic fatty liver disease, and 13.0% to alcoholic liver disease (ALD). Of the cirrhoses, 73.4% were attributable to viral causes and 18.1% to alcohol. Of the hepatocellular carcinoma cases, 86.6% were attributed to viral hepatitis and 11.6% to ALD. Among the benign tumors, hemangioma comprised 52.2% and cystic liver disease comprised 33.7%.ConclusionsKnowledge of the current status of the type and cause of liver disease in Korea may be valuable as a basis for evaluating changing trends in liver disease in that country

The genome sequence of Xanthomonas oryzae pathovar oryzae KACC10331, the bacterial blight pathogen of rice

The nucleotide sequence was determined for the genome of Xanthomonas oryzae pathovar oryzae (Xoo) KACC10331, a bacterium that causes bacterial blight in rice (Oryza sativa L.). The genome is comprised of a single, 4 941 439 bp, circular chromosome that is G + C rich (63.7%). The genome includes 4637 open reading frames (ORFs) of which 3340 (72.0%) could be assigned putative function. Orthologs for 80% of the predicted Xoo genes were found in the previously reported X.axonopodis pv. citri (Xac) and X.campestris pv. campestris (Xcc) genomes, but 245 genes apparently specific to Xoo were identified. Xoo genes likely to be associated with pathogenesis include eight with similarity to Xanthomonas avirulence (avr) genes, a set of hypersensitive reaction and pathogenicity (hrp) genes, genes for exopolysaccharide production, and genes encoding extracellular plant cell wall-degrading enzymes. The presence of these genes provides insights into the interactions of this pathogen with its gramineous host