Special Issues Now Accepting Submissions
Submission Deadline: 1 March, 2021
This special issue focuses on smart sensors challenges in IoMT, and solutions that leverage techniques and insights from the domains of artificial intelligence, edge computing, and IoT. Specifically, it also solicits high quality contributions investigating the usage of biometric signals in the context of IoMT for continuous monitoring for patient-centric healthcare.
Submission Deadline: 15 Nov, 2020
As the coronavirus pandemic deepens, lots of people lose their jobs and normal pace of life, resulting in lots of negative emotions, such as nervous, anxious, sleepless and depressed. There is an urgent demand to pay more attention to psychological health for human wellness by providing methods and means of sensing psychological parameters, emotion care and mental disorder patient monitoring, especially during these difficult times. With the aid of wearable computing technology and artificial intelligence, emotion and mental disorder detections are available through sensing and analyzing psychological parameters. Wearable sensors can collect multimodal data, such as physiological data of human body and psychological data closely related to emotion, including ECG, EEG, blood pressure, blood oxygen, etc. Combining with conventional data, such as video, audio and speech text data, significant mental health characteristics can be obtained using deep learning technology. Though sensing psychological parameters and AI-enabled emotion care are expected to play a major role in improving human wellness, it faces a lot of challenges, such as psychological data processing and analysis, AI-based emotion monitoring and care, etc. Therefore, the main objective of this special issue is to presenting and highlighting the advances and latest novel and emergent technologies, implementations, applications concerning the sensing psychological parameters, emotion care and mental disorder patient monitoring.
Submission Deadline: 1 Feb, 2021
Augmented Reality is a key technology that will facilitate a major paradigm shift in the way users interact with data and has only just recently been recognized as a viable solution for solving many critical needs, especially in the age of artificial intelligence (AI). AR can be used to visualize data from hundreds of sensors simultaneously, overlaying relevant and actionable information over your environment through a headset. Bioinformatics-related research produces huge heterogeneous amounts of data. This wealth of information includes data describing metabolic mechanisms and pathways, proteomics, transcriptomics, and metabolomics. In summary, AR is a cool upcoming wave that will be associated with Bioinformatics, where the vast repositories of data will enable an AR lens into the scenarios in ways that provide near-immediate insight at a level of depth unimaginable previously. As a result, this special session aims to bring the latest results over Bioinformatics and Augmented Reality technologies for both academia and industry. It can help technicians to exchange the latest technical progress.
Submission Deadline: 31 Dec, 2020
Generative adversarial networks (GANs) have received broad interest in computer vision due to their capability for data generation or data translation. Currently, GAN has been rapidly adopted in many applications cross healthcare and biomedicine, addressing problems in image reconstruction, segmentation, classification, and cross-modality synthesis. Despite GAN substantial progress in these areas, their application to medical image computing still faces several challenges. For example, how to synthesize realistic or physically-plausible imagery from small datasets? What are the best GAN architectures and loss functions for specific image computing tasks? When is possible to conduct unsupervised/weak versus supervised deep learning? How to deal with noisy and incomplete data? How to deal with data that is only partially labelled or annotated? How to ensure that learning from GAN-synthesized data generalizes to real-world data? How to develop GAN architectures that integrate biomedical imaging with other biomedical data like omics, radiological text reports, electronic health records, etc.? The goal of this special issue is to attract and highlight the latest developments in GANs for biomedical data processing, and overview the state-of-the-art methods and algorithms at the forefront of using GANs in biomedical image computing.
Submission Deadline: 31 Oct, 2020
There has been tremendous growth in the scale and complexity of biomedical data in the past decade, creating new challenges for analyzing such big data. The focus of this Special Issue will be focused on recent advances in algorithms and analysis tools in biomedical informatics with an emphasis on emerging data types and technologies. It will feature the extended version of some of the best papers selected from ACM BCB 2019. The Special Issue will be open only to invited papers from ACM BCB 2019.
Submission Deadline: 1 Mar, 2021
Due to the proliferation of biomedical imaging modalities such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, and Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography and Atomic Force Microscopy, massive amounts of biomedical and health informatics data are being generated on a daily basis. How can we utilize such big data to build better health profiles and better predictive models so that we can better diagnose and treat diseases and provide a better life for humans? In the past years, many successful learning methods such as deep learning were proposed to answer this crucial question, which has social, economic, as well as legal implications.
A number of significant problems plague the processing of big biomedical and health informatics data, such as data heterogeneity, data incompleteness, data imbalance, and high dimensionality. What is worse is that many data sets exhibit multiple such problems. A majority of existing learning methods can only deal with homogeneous, complete, class-balanced, and moderate-dimensional data. Therefore, data preprocessing techniques including data representation learning, dimensionality reduction, and missing value imputation should be developed to enhance the applicability of deep learning methods in real-world applications of biomedicine and health informatics.
This special issue aims to provide a diverse, but complementary set of contributions to demonstrate new developments and applications that covers existing above issues in data processing of big biomedical and health informatics data. We would also like to accept successful applications of the new methods, including but not limited to data processing, analysis and knowledge discovery of biomedical and health informatics data.
Submission Deadline: 31 Dec, 2020
The Internet of Medical Things (IoMT), which includes medical devices, wearable devices, sensors and apps, is a critical piece of the digital transformation of healthcare, as it allows new business models to emerge and enables changes in work processes, productivity improvements, cost containment and enhanced customer experiences. IoMT can help monitor, inform and notify not only care-givers, but provide healthcare providers with actual data to identify issues before they become critical or to allow for earlier invention. While IoMT offers enormous benefits, the ubiquitously connected devices also pose new challenges. On the one hand, there has been a great improvement in cyberinfrastructure in the era of Industry 4.0, which enables high-frequency long term observational medical data being collected with the help of IoMT. How to convert these data into relevant critical insights that can then be used to provide better care poses a great challenge. On the other hand, although IoMT applications can run well on exiting wireless communication technology, i.e., 4G LTE, there will be others in the future that will require single-digit milliseconds latency and massive bandwidth such as telesurgery. To tackle these challenges, integration AI and 5G into IoMT may achieve an elegant breakthrough in terms of seamless interoperability, low cost, high speed, and low latency, and increased efficiency. Considering the benefit of AI and 5G for IoMT, various AI/5G empowered frameworks/architectures/systems for smart healthcare have been proposed. Even though these approaches have achieved certain success, there exist various scientific and engineering challenges. These open issues call for extensive attention from both academia and industry.
Submission Deadline: 31 Dec, 2020
Nowadays, all over the world, the number of Information and Communication Technology (ICT) investments in health and well-being is rapidly increasing. In this context, there is a growing interest about telehealthcare that allows the provisioning of various kinds of health-related services and applications over the Internet.
This special issue aims to attract contributions from both academic and industrial organizations focusing on the application of emerging ICT (such as Internet of Things (IoT), Cloud/Edge/Fog computing, Artificial Intelligence (AI), Blockchain, etc.) for addressing Telehealthcare issues. Topics of interest include, but are not limited to, the following: emerging architectures and technologies for telehealth; computer aided clinical diagnosis and therapy; networked applications for telehealth; medical signal and data processing; algorithms for decision support and therapy improvement; artificial intelligence applications for telehealth; and advanced security techniques for telehealth.
Submission Deadline: 24 Aug, 2020
Modern medicine and healthcare have become more complex and less explainable and interpretable than ever. Artificial Intelligence (AI) and AI-based automated recommendations and actions have increased dramatically in every aspect of human life. Reliance on AI to automate disease detection, diagnosis, and prediction, and informed decision-making is also on the rise in all fields of medicine. Explainable AI addresses some of the restrictions of black-box AI systems to explain and interpret their diagnosis, predictions, and recommended actions to stakeholders. It aims to create more understandable, interpretable, and reliable models, by improving the quality of predictions.
Submission Deadline: 30 Dec, 2020
Data analytics for Public Health Care has been a pressing challenge for years. Public Health Care organizations must be able to manage, analyze, and interpret data in order to identify the best ways to deliver high quality care. There are a wide range of tools for data analytics in health care, with clinical and operational applications to help organizations capture health data for advancing medical care. Health care data is collected from a variety of systems and devices, such as online patient portals, electronic medical records and health tracking devices. As a result, data exists in different formats, from clinical notes to medical images and at times, the data is unstructured. Data governance covers master data management, which ties master data in a single and reliable source of data to be used for care improvement and patient safety. Data analytics refers to analysis of the data in some way using quantitative and qualitative techniques to be able to explore for trends and patterns in the data. Health data analysts should have the advanced knowledge “to acquire, manage, analyze, interpret, and transform data into accurate, consistent, and timely information”. This special issue aims to consolidate recent advances in data analytics for public health care, research in theory and applications. Pilot studies in analytics-enabled healthcare are especially welcome.
Submission Deadline: Continuous up to Dec. 31st, 2020
On March 12th 2020, the World Health Organization (WHO) announces COVID-19 (COronaVIrus Disease 2019) outbreak as a pandemic. This global pandemic is caused by a new coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was first discovered in December 2019 in China. By April 2020, COVID-19 has spread to more than 200 countries and territories; infected millions of people; and taken more than 200,000 lives globally. Seniors and people with suppressed immune system or chronic diseases are at higher risk. Finally, almost 4 billion of people stay at home.
This Special Issue aims (1) to encourage the stakeholders relating to COVID-19 to share data source, data harmonization, and tools, which can speed up COVID-19 research for years to come; (2) to inspire new informatics method development for rapid testing of virus in humans; (3) to present advanced informatics solutions that utilize machine learning and artificial intelligence methods such as deep learning to analyze COVID-19 data for diagnosis, treatment, and prognosis; (4) to develop computational models and tools to track virus propagation and recurrence; and (5) to model outbreaks for policy makers for better decision making. Informatics goals include data harmonization, data quality control, multi-modality data integration, advanced analysis pipeline such as deep learning, causal inference, real-time decision making, and interpretable models. Researchers, who are using informatics to address COVID – 19 issues are encouraged to submit high quality data and unpublished work. The submitted manuscripts will be processed through a fast track procedure, and the time from submission to first decision will be limited to 15 days.
Submission Deadline: 31 January, 2021
In recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence, brings many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Meanwhile, these technologies have also brought new challenges and issues. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. The practice of biomedical intelligence is based on the prescriptive and predictive analytics of Big data. Biomedical intelligence systems include hardware, computational models, databases, and software that optimize the acquisition, transmission, processing, storage, retrieval, analysis, and interpretation of vast volumes of multi-modal health-related data. Currently, these systems have been deployed in solutions that integrate a variety of technologies, including machine learning (especially deep learning), artificial intelligence, computer vision, Internet of Things, E-Health, bioinformatics, sensors, etc., to achieve patient-centric healthcare. It is expected that the efficiency, accuracy, predictive value, and benefits of biomedical intelligence will greatly improve in the years to come. Researchers from academic fields and industries worldwide are encouraged to submit high quality unpublished original research articles as well as review articles in broad areas relevant to Multi-modal Computing theories and technologies for Biomedical Intelligence Systems.
Submission Deadline: 1 Aug, 2020
Measuring physiological signals from the human face and body using cameras is an emerging research topic in recent years. Various human vital signs can be measured without skin contact, which is convenient and comfortable for long-term continuous health monitoring. The use of cameras also enables the measurement of human behaviors/activities and high-level visual semantic/contextual information, which facilitates understanding of human and scene during the monitoring. Camera-based monitoring will provide pervasive health informatics via a rich set of health monitoring applications in clinical and free-living environments, and directly improve upon contact-based monitoring solutions and impact people’s care experience and quality of life. This special issue highlights recent developments in camera-based health monitoring and unites researchers from multi-disciplinary fields that may contribute to this topic, such as the contributions from the International Workshop on Computer Vision for Physiological Measurement (CVPM) that is collocated with the top IEEE conferences (CVPR/ICCV) in recent years.
Submission Deadline: 31 Dec, 2019
Cerebro-cardiovascular diseases include a variety of medical conditions that affect the blood vessels of the brain, the cerebral circulation, and the heart. Cerebro-cardiovascular diseases are the leading cause of death globally. A rapidly-growing field, biomedical and health engineering research for cerebro-cardiovascular diseases is unique in that it involves a variety of specialties such as neurology, internal medicine, surgery, radiology, epidemiology, cardiology, hematology, psychology and rehabilitation, and must meet the growing need for sophisticated, up-to-date biomedical and health informatics on clinical data, diagnostic testing, and therapeutic issues. The goal of this special issue is to publish the latest technology advancement in flexible sensing and medical imaging for cerebro-cardiovascular health. The special issue focuses on the cross-disciplinary approaches, solutions, and initiatives in imaging informatics, sensor informatics, and medical informatics. The application scenarios can cover single or multiple scenarios of health engineering such as unobtrusive physiological sensing, preventive care, multi-modal fast biomedical imaging and processing, health informatics for precision medicine. While review papers are not excluded, only unpublished original articles will be accepted.
Submission Deadline: 7 Jan, 2020
Eye diseases are leading causes of vision impairment and blindness worldwide. Diagnostic and interventional eye imaging is key technology transforming eye care and treatment. Recently, deep learning and increasing availability of ever-larger scale databases have led to actively exploring new approaches to ocular image analysis and commercial systems, which have shown impressive performance. Increasing interest has also been developed in understanding retinal vasculature and neuro-retinal architecture as a source of biomarkers for several high-prevalence conditions. However, considerable challenges remain in terms of new imaging methods and systems for ophthalmology, reliability and validation of ophthalmic imaging biomarkers, cross-modal image， cross-organ image analysis, methods for more interpretable and explainable machine learning in ophthalmic image analysis and informatics. This special issue will overview the state-of-the-art methods and algorithms at the forefront of ocular image analysis and informatics. We are open to novelty ideas and significant results in the spirit of artificial intelligence especially deep learning from large scale data and multiple modalities.
Submission Deadline EXTENDED: 6 December 2019
Computational Pathology embodies the synergy of Digital Pathology, Medical Image Analysis, Computer Vision, and Machine Learning. The huge amount of information and data available in multi-gigapixel histopathology images makes digital pathology the perfect use case for advanced image analysis techniques. For this reason, deep learning and artificial intelligence have successfully powered computational pathology research in recent years. The goal of this special issue is to attract and highlight the latest developments in computational pathology, and feature papers proposing state-of-the-art solutions in the field of digital pathology using advanced image analysis and artificial intelligence.
Upcoming Special Issues
Submission Deadline: 31 October 2019
Recently, due to the unmet need to address the grand challenges in preventive medicine and the advances in information and computer technologies, the medical and health care are making a paradigm shift from hospital-centred to patient-centred, and from disease-focus to health-focus. With the increasing demand for lower-cost, more convenient, and smarter healthcare solutions, extensive research has been dedicated to the development of novel Internet of Medical Things (IoMT) devices, circuits, systems, platforms, and their applications for health engineering, which are leading to a new and promising healthcare strategy transform. In IoMT, the enabling technologies such as smart biosensors and bioelectronics, wearable and flexible devices, lab-on-a-chip integration, big data collection, analytics, mining and fusion, communication, as well as proactive health management are all paving the way for this new strategy. Considering this situation, this special issue is dedicated to the state-of-the-art health engineering related topics and emphasizes the interdisciplinary bioelectronics and bioinformatics-related topics for health informatics and engineering. Through a collection of original and invited papers, this issue aims to promote the awareness of IoMT technologies in the community of healthcare, and encourage the research collaboration across the fields to address the critical and urgent healthcare concerns.
Submission Deadline: 15 November 2019
A shift toward a data-driven socio-economic health model is occurring as a result of the increased volume, velocity and variety of data collected from the public and private sector involved in health care and science. In this context, the last five-year period has seen an impressive revolution in the theory and application of computational intelligence and informatics in health and biomedical science.
However, the effective use of data to address the scale and scope of human health problems has yet to realize its full potential. The barriers limiting the impact of practical application of standard data mining and machine learning methods are inherent to the “big data” characteristics that, besides the volume of the data, can be summarized in the challenges of data heterogeneity, complexity, variability and dynamic nature together with data management and interpretability of the results.
The scope of this Special issue will be to discuss challenges and opportunities inherent in biological data science, with particular focus on the infrastructure, software, methods and algorithms needed to analyse large data sets in biological and clinical research.
Submission Deadline: 15 November 2019
The topics of integrative sensor networks, informatics and modeling bring together the tightly coupled and rapidly developing fields of biomedical and health informatics and body sensor networks. Biomedical and health informatics encompasses methods to extract and communicate information from data in order to impact health, healthcare, life sciences and biomedicine. Body sensor networks provide one means to measure the needed data, through continuous monitoring in both clinical and free-living environments. This special issue seeks to highlight recent developments in these areas, especially including work presented at the 2019 IEEE-EMBS International Conferences on Biomedical and Health Informatics (BHI’19) and Wearable and Implantable Body Sensor Networks.
Submission Deadline: 30 September 2019
The fourth revolution in healthcare technologies (Healthcare 4.0) is emerging which is powered by the technologies originated from manufacturing industries driven by the fourth revolution of industry (Industry 4.0). In the context of Healthcare 4.0, vast amount of cyber and physical systems (CPS) are closely combined through the Internet of Things (IoT), intelligent sensing, big data analytics, artificial intelligence (AI), cloud computing, automatic control, and autonomous execution and robotics to create not only digitized healthcare products and technologies, but also digitized healthcare services and enterprises. Driven by these mega trends, Health Engineering as a new interdisciplinary field of research and development is emerging, focusing on the applications of engineering principles and efficient and economical approaches to solve problems in healthcare and well-being. Health Engineering will lead to a revolutionized healthcare system that enables the participation of all people for the early prediction and prevention of diseases so that preemptive and pro-active treatment can be delivered to realize personalized, precision, pervasive, and patient-centralized healthcare. This special issue seeks to present the technological advancements of the enabling technologies in Health Engineering for the new revolution of Healthcare 4.0.
Submission Deadline: 1 October 2019
With the development of society, health has received increasing attention. The development of science and technology has also promoted the protection of health. In recent years, the rapid development of computing and networking technologies has improved the ability to collect, measure, and analyze health-related data, and thus tremendous opportunities have opened up for healthcare computing. Meanwhile, these technologies have also brought new challenges and issues. For example, patients’ diagnostic records stored in hospital management systems may be tampered with, which may affect the patients’ health management and insurance compensation. Blockchain has received increasing attention from academia and industry in recent years. It enables transparent interactions of different parties in a more secure and trusted network. The traceability of blockchain allows data to be retained on the blockchain from every step of the data generation process, to endorse the quality of the data and to ensure the correctness of data analysis and mining. The content recorded in the blockchain cannot be tampered with, so it can be used to record important information in health management, provide accurate and reliable health knowledge for network users, and provide accurate information for auditing.
Submission Deadline: 31 May 2019
Among different imaging modalities, ultrasound is the most widespread modality for visualizing human tissue, because of its advantages compared to others: cheap, harmless (no ionizing radiations), allowing real-time feedback, convenient to operate, and well established technology present in all place. Also because of these benefits, tons of medical images are being generated from ultrasound devices. On the other hand, ultrasound images suffer from the disadvantage of being user dependent and noisy which makes the interpretation of US images is sometimes difficult. This special issue seeks to present and highlight the latest development on applying advanced deep learning techniques in ultrasound imaging.
Submission Deadline EXTENDED: 15 February 2019
Predictive methods leverage the data currently available to predict observations at earlier or later time-points. It would constitute a stunning progress in the biomedical data analysis and health informatics research community if, in a few years, we contribute to engineering ‘predictive intelligence’ methods, which can map low- and high-dimensional biomedical data onto the future scores with high precision. Despite the terrific progress that analytical methods have made in the last twenty years in medical image segmentation, registration or other related applications, efficient predictive intelligent models are somewhat lagging behind. Predictive analysis of the disease/disorder progression in patients can have far-reaching consequences for the development of new treatment procedures and novel tools in health informatics, and this is likely to do so exponentially in the coming years. The goal of this Special Issue is to publish original manuscripts and the latest research advancements in different aspects of biomedical, health informatics, and medical image analysis, where predictive methods in artificial intelligence, deep learning, and computer vision intersect with healthcare and life sciences. This Special Issue is conducted in cooperation with the 1st international workshop on PRedictive Intelligence in MEdicine (PRIME 2018).
Submission Deadline EXTENDED: 15 January 2019
Medical data exists in a broad range of formats, from structured data and medical reports to 1D signals, 2D images and 3D volumes or even higher dimensional data such as temporal 3D sequences. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. This high and diverse amount of information needs to be organized and mined in an appropriate way so that meaningful information can be extracted. Several questions, however, arise when dealing with these situations. Should different types of information be treated differently? Should a common framework be derived? Are new analytic approaches needed? It is our hope that these and other questions will be addressed by this special issue. In this call, we thus focus on sharing recent advances in algorithms and applications that involve combining multiple sources of medical information. Topics appropriate for this special issue include novel supervised, unsupervised, semi-supervised and reinforcement algorithms, new architectures, new formulations, and applications related to medical information fusion.
Submission Deadline: 31 December 2018
Mental health is one of the major global health issues affecting substantially more people than other non-communicable diseases. Recent advances in imaging and sensing have facilitated the acquisition of detailed neurological signals and imaging techniques for better understanding of the disorder. In addition, new wearable technologies have enabled continuous sensing of neurological, physiological, and behavioural information of the users. These technologies have led to new insights into mental illnesses providing the needed data to improve the diagnosis, identify triggers of episodes, and enable preventative interventions with diverse machine learning approaches. This special issue is dedicated to cover the related topics on technological advancements for mental health care and diagnosis with focus on pervasive sensing and machine learning.
Submission Deadline EXTENDED: 30 June 2018
Virtual and augmented reality are computational technologies that provide artificial sensory feedback, allowing a subject to experiment activities and events similar to those that can be found in real life and to develop motor and cognitive abilities in immersive three-dimensional environments that resemble the real world, besides being economically viable. Virtual systems with clinical purposes have an important role in health care: they are easily manipulated by specialists as well as by patients, acting as a motivational source for continued treatment that is less aggressive and tedious than traditional treatments, thus, be an interesting approach as a complement and alternative to conventional treatment for these patients, establishing a new standard in the individual’s rehabilitation strategy. This special issue is based on the technological advances considered in the process of neurorehabilitation using virtual environments, serious games among other technologies for a playful, non-invasive treatment and that has shown to be quite efficient and effective in improving the clinical condition of the patients and their (re) insertion into society. Furthermore, it aims to introduce the recent progress of virtual environments in Neuroscience and addresses the challenges in developing dedicated systems for various clinical applications, while proposing new ideas and directions for future development.
Submission Deadline EXTENDED: 30 June, 2018
A pre-requisite for achieving the vision for more precise and personalized diagnostics and treatment and high-quality cancer care concerns the development of learning health information management systems that enable real-time analysis of data from cancer patients in a variety of care settings. The most often cited challenges are related to the intrinsic complexity of the underlying biomedical and clinical data and the fact that information exists in both structured and unstructured formats. Inevitably, initiatives and advances in big data analytics are an important domain of discussion in our quest for understanding how the cancer genome changes in time, but also for discovering novel predictive/prognostic biomarkers and novel potential therapeutic targets. In addition, as cancer is more and more changing to a chronic disease, tools that would empower cancer patients in self-management are clearly needed.
This Special Issue will address current advances on various fronts, focusing on reporting bioinformatics, analysis of molecular, genetic and/or clinical data pertaining to human cancer risk, prevention, outcomes or treatment response. Also, the issue will seek contributions presenting current approaches for the development of oncology decision-support solutions that offer seamless data integration across specialties and locations, data-driven decision making, and tools for proactive patient involvement.
Submission Deadline EXTENDED: 14 May 2018
Today, on one hand, software frameworks for deep-learning are becoming increasingly capable of training advanced neural-network models, while on the other hand, heterogeneous hardware components such as GPUs, FPGAs and ASICs dedicated to deep learning are beginning to challenge the computational limits of Moore’s law. Together, these trends have influenced connected-health informatic systems, which comprise various processes for sensing, data transfer, storage and analytics to improve overall health and wellbeing. Increasingly, each of these processes are being infused with artificial intelligence (AI), leading to unprecedented advances in how automated care is being delivered. This automation has helped engineers shift focus from mundane issues like feature optimization to productive ones like understanding clinical relevance and evaluating strategies for responsive health care.
This special issue aims to bring the spotlight on AI techniques that have helped advance connected-health informatics. Topics range from technical issues like AI theory, algorithms and data-management to application-oriented contributions that push forward automation in assistive robots, preventative health and pharmaceutical care.
Submission Deadline EXTENDED: 31 March 2018
Machine learning plays an essential role in the field of medical imaging and image informatics. There are numerous challenges, including diverse and inhomogeneous inputs, high dimensional features versus inadequate subjects, subtle key pattern hidden by large individual variation, and sometimes an unknown mechanism underlying the disease. Inspired by the challenges and also the chances, more and more people are devoting to the research direction of machine learning in medical imaging nowadays. The goal of this Special Issue is to publish the latest research advancements in integrating machine learning with medical images and health informatics.
This special issue is in cooperation with the 8th international workshop of Machine Learning in Medical Imaging (MLMI 2017), and goes beyond.
Submission Deadline EXTENDED: 28 February 2018
Recent advances in information and communication technologies (ICT) have acted as catalysts for significant developments in the sector of health care, affecting strongly medical diagnosis, patient and healthcare management, treatment and health education. Small wearable, disposable sensors or medical devices as well as elementary services are featured as keys for monitoring health and facilitating well-being. The Internet of “small” Things (IoT) is at its infancy, and it will slowly but surely play a pivotal role in the monitoring of health, in early diagnosis/prognosis, in prompt design of interventions, and their precise personalisation.
The proposed special issue aims at attracting contributions on the aforementioned research areas and technologies, focusing on how they can be applied to personalising Digital Medical Systems. The goal of this special issue is to publish the latest research advances on the research and application of Internet of small Things; knowledge discovery and knowledge representation for the analysis of Big Data and the role of Massive Open Education on acceptance towards personalizing Digital Medical Systems. Only articles from contributors to the recent 30th IEEE International Symposium on Computer Based Medical Systems (IEEE-CBMS2017) will be considered.
Submission Deadline EXTENDED: 15 January 2018
Neuro-Informatics is one of the most attractive research topics for many generations of Scientists, Engineers, Practitioners, Physicians, others due to its profound importance in healthcare and in our lives, as well. Significant driving forces behind of such research topic are, human curiosity, the BRAIN Project in USA with a very large funding budget, the exponential evolution of the IT (or Computational Informatics) and Nano-Tech the last two decades, which have inspired and motivated many researchers around the globe to contribute with their research to the “last frontier (the brain)”.
The main goal for this special issue is to motivate researchers and scientists to contribute to Neuro-Ιnformatics and associated fields with state of the art methodologies and devices that may lead to new discoveries for diseases, deficiencies and early prognosis.
Submission Deadline: 15 January 2018
This Special Issue focuses on biomedical data, providing the readers with state of the art and insight on how biomedical data can be used to infer knowledge for diagnosis and treatment of diseases, to reason for diagnosis prediction, and to represent heterogeneous information.
Submission Deadline: 15 January 2018
In the majority of medical conditions common therapeutic approaches are usually effective in only a small percentage of the patient population. It has become apparent that in order to improve the response to therapy and long term prognosis, treatment must be specifically tailored to the disease and the patient. Precision medicine is an attempt to maximize effectiveness by taking into account individual variability in clinical presentation, medical history, genes, environment, and lifestyle.
The purpose of this special issue is to report the latest advances in the field of integrated precision medicine technologies to further enable, drive and accelerate the development, translation, and application of precision medicine. Topics for this special issue include, but are not limited to: Bioinformatics, Imaging Informatics, Sensor Informatics, and Medical Informatics and Public Health Informatics.
Submission Deadline: 31 December 2017
In the era of Industry4.0, deep convergence of automation technologies, biomedical engineering, and health informatics is reshaping the research landscape towards the rapid development of Health Engineering, an emerging interdisciplinary field for the predictive, preventive, precise and personalized medicine. This has offered an unpresented opportunity for solving the challenges caused by the aging of population.
This special issue will focus on the cross disciplinary approaches, solutions, and initiatives for aging population enabled by the convergence of automation technologies, biomedical engineering and health informatics. The application scenarios can cover single or multiple scenarios of health engineering such as primary care, preventive care, predictive technologies, hospitalization, home care, and occupational health.
Invasive and in-situ malignant melanoma together comprise one of the most rapidly increasing cancers in the world. Invasive melanoma alone has an estimated incidence of 87,110 and of 9,730 deaths in the United States in 2017. Early diagnosis is critical, as melanoma can be effectively treated with simple excision if detected early. The goals of this special issue are to summarize the state-of-the-art in both the computerized analysis of skin lesion images, as well as image acquisition technologies, providing future directions for this exciting subfield of medical image analysis. Topics of interest include, but are not limited to: novel and emerging imaging technologies, image enhancement, image registration, image segmentation, feature extraction, image classification, and hardware systems. The intended audience includes researchers and practicing clinicians, who are increasingly using digital analytic tools.