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The Effect of Quality Control on Accuracy of Digital Pathology Image Analysis IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-12-21 Alexander I. Wright; Catriona M. Dunn; Michael Hale; Gordon G. A. Hutchins; Darren E. Treanor
Digital slide images produced from routine diagnostic histopathological preparations suffer from variation arising at every step of the processing pipeline. Typically, pathologists compensate for such variation using expert knowledge and experience, which is difficult to replicate in automated solutions. The extent to which inconsistencies affect image analysis is explored in this work, examining in
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COVID-19 CT Image Synthesis With a Conditional Generative Adversarial Network IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-12-04 Yifan Jiang; Han Chen; Murray Loew; Hanseok Ko
Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection
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[Front cover] IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-12-04
Presents the front cover for this issue of the publication.
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IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-12-04
Provides a listing of current staff, committee members and society officers.
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Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-12 Dufan Wu; Kuang Gong; Chiara Daniela Arru; Fatemeh Homayounieh; Bernardo Bizzo; Varun Buch; Hui Ren; Kyungsang Kim; Nir Neumark; Pengcheng Xu; Zhiyuan Liu; Wei Fang; Nuobei Xie; Won Young Tak; Soo Young Park; Yu Rim Lee; Min Kyu Kang; Jung Gil Park; Alessandro Carriero; Luca Saba; Mahsa Masjedi; Hamidreza Talari; Rosa Babaei; Hadi Karimi Mobin; Shadi Ebrahimian; Ittai Dayan; Mannudeep K. Kalra; Quanzheng
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be
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M$^3$Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-13 Xuelin Qian; Huazhu Fu; Weiya Shi; Tao Chen; Yanwei Fu; Fei Shan; Xiangyang Xue
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources ( e.g , time and budget), we propose a Multi-task Multi-slice Deep Learning System (M $^3$ Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which
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A Novel Intelligent Computational Approach to Model Epidemiological Trends and Assess the Impact of Non-Pharmacological Interventions for COVID-19 IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-30 Jinchang Ren; Yijun Yan; Huimin Zhao; Ping Ma; Jaime Zabalza; Zain Hussain; Shaoming Luo; Qingyun Dai; Sophia Zhao; Aziz Sheikh; Amir Hussain; Huakang Li
The novel coronavirus disease 2019 (COVID-19) pandemic has led to a worldwide crisis in public health. It is crucial we understand the epidemiological trends and impact of non-pharmacological interventions (NPIs), such as lockdowns for effective management of the disease and control of its spread. We develop and validate a novel intelligent computational model to predict epidemiological trends of COVID-19
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A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-Center Study IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-27 Lingwei Meng; Di Dong; Liang Li; Meng Niu; Yan Bai; Meiyun Wang; Xiaoming Qiu; Yunfei Zha; Jie Tian
Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We retrospectively collected 366 severe or critical COVID-19 patients from four centers, including 70 patients who died within 14 days (labeled as high-risk
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Classification of Severe and Critical Covid-19 Using Deep Learning and Radiomics IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-09 Cong Li; Di Dong; Liang Li; Wei Gong; Xiaohu Li; Yan Bai; Meiyun Wang; Zhenhua Hu; Yunfei Zha; Jie Tian
Objective: The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images. Methods: We retrospectively enrolled 217 patients from three centers in China, including 82 patients
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COVIDGR Dataset and COVID-SDNet Methodology for Predicting COVID-19 Based on Chest X-Ray Images IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-10 S. Tabik; A. Gómez-Ríos; J. L. Martín-Rodríguez; I. Sevillano-García; M. Rey-Area; D. Charte; E. Guirado; J. L. Suárez; J. Luengo; M. A. Valero-González; P. García-Villanova; E. Olmedo-Sánchez; F. Herrera
Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning
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IEEE Journal of Biomedical and Health Informatics (J-BHI) IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-12-04
Provides a listing of current committee members and society officers.
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[Blank page] IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-12-04
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Deep Learning Methods for Lung Cancer Segmentation in Whole-Slide Histopathology Images—The ACDC@LungHP Challenge 2019 IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-20 Zhang Li; Jiehua Zhang; Tao Tan; Xichao Teng; Xiaoliang Sun; Hong Zhao; Lihong Liu; Yang Xiao; Byungjae Lee; Yilong Li; Qianni Zhang; Shujiao Sun; Yushan Zheng; Junyu Yan; Ni Li; Yiyu Hong; Junsu Ko; Hyun Jung; Yanling Liu; Yu-cheng Chen; Ching-wei Wang; Vladimir Yurovskiy; Pavel Maevskikh; Vahid Khanagha; Yi Jiang; Li Yu; Zhihong Liu; Daiqiang Li; Peter J. Schüffler; Qifeng Yu; Hui Chen; Yuling Tang;
Accurate segmentation of lung cancer in pathology slides is a critical step in improving patient care. We proposed the ACDC@LungHP (Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology) challenge for evaluating different computer-aided diagnosis (CADs) methods on the automatic diagnosis of lung cancer. The ACDC@LungHP 2019 focused on segmentation (pixel-wise detection) of
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Kidney Level Lupus Nephritis Classification Using Uncertainty Guided Bayesian Convolutional Neural Networks IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-18 Pietro Antonio Cicalese; Aryan Mobiny; Zahed Shahmoradi; Xiongfeng Yi; Chandra Mohan; Hien Van Nguyen
The kidney biopsy based diagnosis of Lupus Nephritis (LN) is characterized by low inter-observer agreement, with misdiagnosis being associated with increased patient morbidity and mortality. Although various Computer Aided Diagnosis (CAD) systems have been developed for other nephrohistopathological applications, little has been done to accurately classify kidneys based on their kidney level Lupus
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Frontcover IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-04
Presents the front cover for this issue of the publication.
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IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-04
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of Contents IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-04
Presents the table of contents for this issue of the publication.
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Guest Editorial Data Science in Smart Healthcare: Challenges and Opportunities IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-04 Barbara Di Camillo; Giuseppe Nicosia; Francesca Buffa; Benny Lo
The fifteen articles in this special section focus on data science used in smart healthcare applications. A shift toward a data-driven socio-economic health model is occurring. This is the result of the increased volume, velocity and variety of data collected from the public and private sector in healthcare, and biology in general. In the past five-years, there has been an impressive development of
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Automatic and Explainable Labeling of Medical Event Logs with Autoencoding. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-04 Hugo DeOliveira,Vincent Augusto,Baptiste Jouaneton,Ludovic Lamarsalle,Martin Prodel,Xiaolan Xie
Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding
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Guest Editorial Flexible Sensing and Medical Imaging for Cerebro-Cardiovascular Health IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-04
The articles in this special section focus on flexible sensing and medical imaging for cerebro-cardiovascular health care services. Healthcare and disease management are receiving increasing attention. Cerebro-cardiovascular diseases (CCVDs) are the leading cause of death globally. Cerebrocardiovascular diseases include a variety of medical conditions that affect the blood vessels of the brain, the
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IEEE Journal of Biomedical and Health Informatics (J-BHI) IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-11-04
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Measuring Domain Shift for Deep Learning in Histopathology IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-21 Karin Stacke; Gabriel Eilertsen; Jonas Unger; Claes Lundström
The high capacity of neural networks allows fitting models to data with high precision, but makes generalization to unseen data a challenge. If a domain shift exists, i.e. differences in image statistics between training and test data, care needs to be taken to ensure reliable deployment in real-world scenarios. In digital pathology, domain shift can be manifested in differences between whole-slide
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Automatically Assessing Quality of Online Health Articles IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-20 Fariha Afsana; Muhammad Ashad Kabir; Naeemul Hassan; Manoranjan Paul
Today Information in the world wide web is overwhelmed by unprecedented quantity of data on versatile topics with varied quality. However, the quality of information disseminated in the field of medicine has been questioned as the negative health consequences of health misinformation can be life-threatening. There is currently no generic automated tool for evaluating the quality of online health information
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Frontcover IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-07
Presents the front cover for this issue of the publication.
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IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-07
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of Contents IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-07
Presents the table of contents for this issue of the publication.
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Editorial Special Issue on “AI-Driven Informatics, Sensing, Imaging and Big Data Analytics for Fighting the COVID-19 Pandemic” IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-07 Amir Amini; Wei Chen; Giancarlo Fortino; Ye Li; Yi Pan; May Dongmei Wang
The papers in this special section focuses on artificial intelligent-driven informatics, sensing, imaging and big data analytics in dealing with the COVID-19 pandemic.
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Efficient and Effective Training of COVID-19 Classification Networks with Self-supervised Dual-track Learning to Rank. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-08-20 Yuexiang Li,Wei Dong,Jiawei Chen,Shelei Cao,Hongyu Zhou,Yanchun Zhu,Jianrong Wu,Lan Lan,Wenbo Sun,Tianyi Qian,Kai Ma,Haibo Xu,Yefeng Zheng
Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremendous pressure on radiologists for reading the exams and may potentially lead to fatigue-related
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Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-08-26 Liang Sun,Zhanhao Mo,Fuhua Yan,Liming Xia,Fei Shan,Zhongxiang Ding,Bin Song,Wanchun Gao,Wei Shao,Feng Shi,Huan Yuan,Huiting Jiang,Dijia Wu,Ying Wei,Yaozong Gao,He Sui,Daoqiang Zhang,Dinggang Shen
Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an A daptive F eature S election guided D eep F orest (AFS-DF) for COVID-19
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Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-10 Zhao Wang,Quande Liu,Qi Dou
The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning
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IEEE Journal of Biomedical and Health Informatics (J-BHI) IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-07
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Blank Page IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-10-07
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Attention-Guided Multi-Branch Convolutional Neural Network for Mitosis Detection From Histopathological Images IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-29 Haijun Lei; Shaomin Liu; Ahmed Elazab; Xuehao Gong; Baiying Lei
Mitotic count is an important indicator for assessing the invasiveness of breast cancers. Currently, the number of mitoses is manually counted by pathologists, which is both tedious and time-consuming. To address this situation, we propose a fast and accurate method to automatically detect mitosis from the histopathological images. The proposed method can automatically identify mitotic candidates from
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Tear Film Classification in Interferometry Eye Images Using Phylogenetic Diversity Indexes and Ripley's K Function IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-25 Luana Batista da Cruz; Johnatan Carvalho Souza; Anselmo Cardoso de Paiva; João Dallyson Sousa de Almeida; Geraldo Braz Junior; Kelson Rômulo Teixeira Aires; Aristófanes Corrêa Silva; Marcelo Gattass
Dry eye syndrome is one of the most frequently reported eye diseases in ophthalmological practice. The diagnosis of this disease is a challenging task due to its multifactorial etiology. One of the most applied tests is the manual classification of tear film images captured with the Doane interferometer. The interference phenomena in these images can be characterized as texture patterns, which can
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A Heart Rate Monitoring Framework for Real-World Drivers Using Remote Photoplethysmography. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-24 Po-Wei Huang,Bing-Jhang Wu,Bing-Fei Wu
Remote photoplethysmography (rPPG) is an unobtrusive solution to heart rate monitoring in drivers. However, disturbances that occur during driving such as driver behavior, motion artifacts, and illuminance variation complicate the monitoring of heart rate. Faced with disturbance, one commonly used assumption is heart rate periodicity (or spectrum sparsity). Several methods improve stability at the
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Improved 3D Catheter Shape Estimation using Ultrasound Imaging for Endovascular Navigation: A Further Study. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-23 Fang Chen,Jia Liu,Xinran Zhang,Daoqiang Zhang,Hongen Liao
Objective: Two-dimensional fluoroscopy is the standard guidance imaging method for closed endovascular intervention. However, two-dimensional fluoroscopy lacks depth perception for the intervention catheter and causes radiation exposure for both surgeons and patients. In this paper, we extend our previous study and develop the improved three-dimensional (3D) catheter shape estimation using ultrasound
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A Privacy-preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-23 Md Whaiduzzaman,Md Razon Hossain,Ahmedur Rahman Shovon,Shanto Roy,Aron Laszka,Rajkumar Buyya,Alistair Barros
To slow down the spread of COVID-19, governments worldwide are trying to identify infected people, and contain the virus by enforcing isolation, and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission, and mass infection. To address this problem, we develop an e-government Privacy-Preserving Mobile, and Fog
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Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients with Asymmetric Network. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-23 Yan Shao,Xiaoying Zhang,Ge Wu,Qingtao Gu,Jiyong Wang,Yanchen Ying,Aihui Feng,Guotong Xie,Qing Kong,Zhiyong Xu
The iterative design of radiotherapy treatment plans is time-consuming and labor-intensive. In order to provide a guidance to treatment planning, Asymmetric network (A-Net) is proposed to predict the optimal 3D dose distribution for lung cancer patients. A-Net was trained and tested in 392 lung cancer cases with the prescription doses of 50Gy and 60Gy. In A-Net, the encoder and decoder are asymmetric
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Modeling texture in deep 3D CNN for survival analysis. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-22 Ahmad Chaddad,Paul Sargos,Christian Desrosiers
Radiomics has shown remarkable potential for predicting the survival outcome for various types of cancers such as pancreatic ductal adenocarcinoma (PDAC). However, to date, there has been limited research using convolutional neural networks (CNN) with radiomic methods for this task, due to their requirement for large training sets. To overcome this issue, we propose a new type of radiomic descriptor
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SleepPoseNet: Multi-View for Sleep Postural Transition Recognition Using UWB. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-22 Maytus Piriyajitakonkij,Patchanon Warin,Payongkit Lakhan,Pitshaporn Leelaarporn,Nakorn Kumchaiseemak,Supasorn Suwajanakorn,Theerasarn Pianpanit,Nattee Niparnan,Subhas Chandra Mukhopadhyay,Theerawit Wilaiprasitporn
Recognizing the movements during sleep is crucial for the monitoring of patients with sleep disorders. However, the utilization of Ultra-Wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates the performance of the off-the-shelf single antenna UWB in a novel application of sleep postural transition (SPT) recognition. The proposed Multi-View
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Low-Dimensional Subject Representation-based Transfer Learning in EEG Decoding. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-22 Po-Yuan Jeng,Chun-Shu Wei,Tzyy-Ping Jung,Li-Chun Wang
Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-learning techniques perform supervised calibration. This kind of calibration approach
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1D Convolutional Neural Networks for Detecting Nystagmus. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-21 Jacob Laurence Newman,John Phillips,Stephen Cox
Vertigo is a type of dizziness characterised by the subjective feeling of movement despite being stationary. One in four individuals in the community experience symptoms of dizziness at any given time, and it can be challenging for clinicians to diagnose the underlying cause. When dizziness is the result of a malfunction in the inner-ear, the eyes flicker and this is called nystagmus. In this article
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Deep Learning for Diagnosis and Segmentation of Pneumothorax: The Results on The Kaggle Competition and Validation Against Radiologists. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-21 Alexey Tolkachev,Ilyas Sirazitdinov,Maksym Kholiavchenko,Tamerlan Mustafaev,Bulat Ibragimov
Pneumothorax is potentially a life-threatening disease that requires urgent diagnosis and treatment. The chest X-ray is the diagnostic modality of choice when the pneumothorax is suspected. Computer-aided diagnosis of pneumothorax has got a dramatic boost in the last years due to deep learning advances and the first public pneumothorax diagnosis competition with 15257 chest X-rays manually annotated
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Wearables and Deep Learning Classify Fall Risk from Gait in Multiple Sclerosis. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-18 Brett M Meyer,Lindsey J Tulipani,Reed D Gurchiek,Dakota A Allen,Lukas Adamowicz,Dale Larie,Andrew J Solomon,Nick Cheney,Ryan McGinnis
Falls are a significant problem for persons with multiple sclerosis (PwMS). Yet fall prevention interventions are not often prescribed until after a fall has been reported to a healthcare provider. While still nascent, objective fall risk assessments could help in prescribing preventative interventions. To this end, retrospective fall status classification commonly serves as an intermediate step in
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Spatiotemporal Gait Measurement with a Side-View Depth Sensor Using Human Joint Proposals. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-18 Andrew Hynes,Stephen Czarnuch,Megan Kirkland,Michelle Ploughman
We propose a method for calculating standard spatiotemporal gait parameters from individual human joints with a side-view depth sensor. Clinical walking trials were measured concurrently by a side-view Kinect and a pressure-sensitive walkway, the Zeno Walkway. Multiple joint proposals were generated from depth images by a stochastic predictor based on the Kinect algorithm. The proposals are represented
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Novel Measures of Similarity and Asymmetry in Upper Limb Activities for Identifying Hemiparetic Severity in Stroke Survivors. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-18 Shreyasi Datta,Chandan Karmakar,Bernard Yan,Marimuthu Swami Palaniswami
Stroke survivors are often characterized by hemiparesis, i.e., paralysis in one half of the body, severely affecting upper limb movements. Monitoring the progression of hemiparesis requires manual observation of limb movements at regular intervals, and hence is a labour intensive process. In this work, we use wrist-worn accelerometers for automated assessment of hemiparesis in acute stroke. We propose
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A multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-18 Runxi Cui,Zhigang Chen,Jia Wu,Yanlin Tan,Genghua Yu
Accurate segmentation and segmentation of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual procedures might lead to inaccurate measurement results. Therefore, we designed a
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Weakly Supervised Histopathology Image Segmentation with Sparse Point Annotations. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-15 Zhe Chen,Zhao Chen,Jingxin Liu,Qiang Zheng,Yuang Zhu,Yanfei Zuo,Zhaoyu Wang,Xiaosong Guan,Yue Wang,Yuan Li
Digital histopathology image segmentation can facilitate computer-assisted cancer diagnostics. Given the difficulty of obtaining manual annotations, weak supervision is more suitable for the task than full supervision is. However, most weakly supervised models are not ideal for handling severe intra-class heterogeneity and inter-class homogeneity in histopathology images. Therefore, we propose a novel
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Multi-Res-Attention UNet : A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-15 Edwin Thomas,S J Pawan,Shushant Kumar,Anmol Horo,S Niyas,S Vinayagamani,Chandrasekharan Kesavadas,Jeny Rajan
In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model
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Automatic Pancreas Segmentation in CT Images with Distance-based Saliency-Aware DenseASPP Network. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-11 Peijun Hu,Xiang Li,Yu Tian,Tianyu Tang,Tianshu Zhou,Xueli Bai,Shiqiang Zhu,Tingbo Liang,Jingsong Li
Pancreas identification and segmentation is an essential task in the diagnosis and prognosis of pancreas disease. Although deep neural networks have been widely applied in abdominal organ segmentation, it is still challenging for small organs (e.g. pancreas) that present low contrast, highly flexible anatomical structure and relatively small region. In recent years, coarse-to-fine methods have improved
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B-Tensor: Brain Connectome Tensor Factorization for Alzheimer's Disease. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-11 Goktekin Durusoy,Zerrin Yildirim,Demet Yuksel Dal,Cigdem Ulasoglu-Yildiz,Elif Kurt,Gunes Bayir,Erhan Ozacar,Evren Ozarslan,Asli Demirtas-Tatlidede,Basar Bilgic,Tamer Demiralp,Hakan Gurvit,Alkan Kabakcioglu,Burak Acar
AD is the highly severe part of the dementia spectrum and impairs cognitive abilities of individuals, bringing economic, societal and psychological burdens beyond the diseased. A promising approach in AD research is the analysis of structural and functional brain connectomes, i.e. sNETs and fNETs, respectively. We propose to use tensor representation (B-tensor) of uni-modal and multi-modal brain connectomes
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3D Retinal Vessel Density Mapping With OCT-Angiography IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-10 Mona Sharifi Sarabi; Maziyar M. Khansari; Jiong Zhang; Sam Kushner-Lenhoff; Jin Kyu Gahm; Yuchuan Qiao; Amir H. Kashani; Yonggang Shi
Optical Coherence Tomography Angiography (OCTA) is a novel, non-invasive imaging modality of retinal capillaries at micron resolution. Recent studies have correlated macular OCTA vascular measures with retinal disease severity and supported their use as a diagnostic tool. However, these measurements mostly rely on a few summary statistics in retinal layers or regions of interest in the two-dimensional
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Automatic Segmentation and Visualization of Choroid in OCT with Knowledge Infused Deep Learning. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-10 Huihong Zhang,Jianlong Yang,Kang Zhou,Fei Li,Yan Hu,Yitian Zhao,Ce Zheng,Xiulan Zhang,Jiang Liu
The choroid provides oxygen and nourishment to the outer retina thus is related to the pathology of various ocular diseases. Optical coherence tomography (OCT) is advantageous in visualizing and quantifying the choroid in vivo . However, its application in the study of the choroid is still limited for two reasons. (1) The lower boundary of the choroid (choroid-sclera interface) in OCT is fuzzy, which
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Corrections to "Human-Computer Interface Controlled by the Lip". IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-04 Marcelo Archanjo Jose,Roseli de Deus Lopes
Presents corrections to author information in the above named paper.
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An Ensemble of Hyperdimensional Classifiers: Hardware-Friendly Short-Latency Seizure Detection with Automatic iEEG Electrode Selection. IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-07 Alessio Burrello,Simone Benatti,Kaspar Anton Schindler,Luca Benini,Abbas Rahimi
We propose an intracranial electroencephalography (iEEG) based algorithm for detecting epileptic seizures with short latency, and with identifying the most relevant electrodes. Our algorithm first extracts three features, namely mean amplitude, line length, and local binary patterns that are fed to an ensemble of classifiers using hyperdimensional (HD) computing. These features are embedded into an
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Frontcover IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-03
Presents the front cover for this issue of the publication.
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IEEE Journal of Biomedical and Health Informatics IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-03
Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
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Table of Contents IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-03
Presents the table of contents for this issue of the publication.
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Guest Editorial Enabling Technologies in Health Engineering and Informatics for the New Revolution of Healthcare 4.0 IEEE J. Biomed. Health Inform. (IF 5.223) Pub Date : 2020-09-03
The eleven papers presented in this special issue provide a snapshot of the latest advances in the field of enabling technologies in health engineering and health informatics for the new revolution of Healthcare 4.0, hoping to further enable, drive and accelerate the research, development, and application of key technologies into healthcare systems.