Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.

Optical pulse detection photoplethysmography (PPG) provides a means of low cost and unobtrusive physiological monitoring that is popular in many wearable devices. However, the accuracy, robustness and generalizability of single-wavelength PPG sensing are sensitive to biological characteristics as well as sensor configuration and placement; this is significant given the increasing adoption of single-wavelength wrist-worn PPG devices in clinical studies and healthcare. Since different wavelengths interact with the skin to varying degrees, researchers have explored the use of multi-wavelength PPG to improve sensing accuracy, robustness and generalizability. This paper contributes a novel and comprehensive state-of-the-art review of wearable multi-wavelength PPG sensing, encompassing motion artifact reduction and estimation of physiological parameters. The paper also encompasses theoretical details about multi-wavelength PPG sensing and the effects of biological characteristics. The review findings highlight the promising developments in motion artifact reduction using multi-wavelength approaches, the effects of skin temperature on PPG sensing, the need for improved diversity in PPG sensing studies and the lack of studies that investigate the combined effects of factors. Recommendations are made for the standardization and completeness of reporting in terms of study design, sensing technology and participant characteristics.

R-BME publishes methodological and clinical reviews on biomedical engineering. Acceptable papers range in content from engineering methodologies or clinical applications, experimental science and technological development, up to formal mathematical theory of interest to biomedical engineering. Review articles must summarize in a clear, critical, and well documented way (i.e., through extensive referencing) the author's opinion about the subject matter and should also offer a vision for possible future developments. Review articles that do not meet this criterion would not be suitable for publication in R-BME. As per IEEE policy on standards for publications, review and editorial handling of manuscripts is part of the paper submission process to guarantee quality control. R-BME will also contain a section entitled "In the Spotlight" that will present brief yearly reviews of themes important for the EMB Society as defined by the themes of its annual international conference. These "In the Spotlight" briefs are written by a Board of Acting Editors, appointed by the Editor-in-Chief for a period of three years. Readers are encouraged to submit manuscripts as Methodological Reviews or Clinical Application Reviews. The goal of Methodological Reviews is to present a critical review of current methods important in biomedical engineering, understood in its most general sense. These methodological reviews will be authored by the top experts in the biomedical engineering field. The most useful reviews are the ones that conceptually relate various methods, present validation among them according to accepted metrics, evaluate pros and cons, and comment on future directions. These papers will be up to 15 pages long with at least 100 references. The writing style should target an audience at the level of the biomedical engineering Ph.D. student. The target audiences are biomedical Ph.D. students, academicians, and design engineers in the industry. The goal of Clinical Application Reviews is to present a critical review of methods applied to a current clinical problem, where biomedical technology is playing a crucial role. The article should be written from an organ/systems physical modeling or clinical perspective; i.e., dealing with the physical properties and/or clinical issues and then covering the methods. These clinical reviews should preferably be authored by a team (e.g., biophysicists, biomedical engineers, and a clinician). The most useful reviews will start by explaining the clinical problem, its variables, and how one of several competing biomedical technologies are playing a role, their pros and cons, discussions of bottlenecks and what needs to be done to overcome them. These manuscripts will be up to 20 pages long, and will have at least 100 references, divided between clinical and bioengineering. The writing style will be at the Scientific American level. The target audiences consist of clinicians, design engineers in the industry, and students/academics belonging to IEEE but not necessarily in biomedical engineering. To qualify for publication, these must be previously unpublished and not be under consideration elsewhere. The manuscript will be evaluated under the category it has been submitted. The Editor-in-Chief will perform a quick review of the manuscript not only to propose a change in category, but also to evaluate the manuscript in terms of novelty, quality, and appropriateness, and reject the manuscript if obvious pitfalls exist. Manuscripts will ONLY be accepted in electronic format through ScholarOne Manuscripts. Please go to the ScholarOne Manuscripts website at http://mc.manuscriptcentral.com/rbme-embs. Do not send original submissions or revisions directly to the Editor-in-Chief. If you are unable to submit your contribution electronically, please contact the Editor-in-Chief at http://www.embs.org Manuscript/Communication Style: For purposes of qualifying for peer review and consideration for publication, authors must submit a properly executed copyright form in favor of IEEE, sign an Author consent form, agree to a mandatory page charge for overlength manuscripts or color figures, and prepare a manuscript according to the following guidelines: 1) The manuscript, may not exceed 40 double-spaced pages (one full blank line between lines of type) using a font size of 11 points or larger, having a margin of at least 1'' on all sides. The manuscripts including the figures, should be converted to postscript or PDF for upload to ScholarOne Manuscripts 2) The abstract must be no more than 200 words. 3) Figures should appear at the back of the text, one figure per page. A separate list of figure captions should be included, keyed to figure numbers marked on each illustration. 4) References should appear in a separate reference section at the end of this paper with items referred to by numerals in square brackets. References must be complete in the IEEE style. You can find an electronic style file at http://www.ieee.org/web/publications/authors/transjnl/index.html 5) If accepted, authors of regular papers are also requested to send biographies (no more than 50 words) and recent photographs (3/4x3/4). 6) All manuscripts accepted for publication will REQUIRE the authors to make the final submission employing the style files. Please see the detailed guidelines at http://www.embs.org.

Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed.

Technologies for quantifying circulating tumour DNA (ctDNA) in liquid biopsies could enable real-time measurements of cancer progression, profoundly impacting patient care. Sequencing methods can be too complex and time-consuming for regular point-of-care monitoring, but nanotechnology offers an alternative, harnessing the unique properties of objects tens to hundreds of nanometres in size. This systematic review was performed to identify all examples of nanotechnology-based ctDNA detection and assess their potential for clinical use. Google Scholar, PubMed, Web of Science, Google Patents, Espacenet and Embase/MEDLINE were searched up to 23rd March 2021. The review identified nanotechnology-based methods for ctDNA detection for which quantitative measures (e.g., limit of detection, LOD) were reported and biologically relevant samples were used. The pre-defined inclusion criteria were met by 66 records. LODs ranged from 10 zM to 50nM. 25 records presented an LOD of 10fM or below. Nanotechnology-based approaches could provide the basis for the next wave of advances in ctDNA diagnostics, enabling analysis at the point-of-care, but none are currently used clinically. Further work is needed in development and validation; trade-offs are expected between different performance measures e.g., number of sequences detected and time to result.

Cochlear implant technology successfully restores hearing function to patients with sensory impairment. Although cochlear implant users generally hear well in quiet, they still find noisy conditions very challenging, hence the need to employ noise reduction algorithms in these systems to enhance the user experience. This paper reviews noise reduction algorithms in cochlear implants. Traditionally, such algorithms have been classified as either single- or multiple-channel, depending on the number of microphones they use. This review retains this general classification in looking at recent papers and extends it to reflect recent interest in machine learning techniques. The review concludes with consideration of promising future areas of research.

Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today's technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.

Cardiovascular disease is a deadly global health crisis that carries a substantial financial burden. Innovative treatment and management of cardiovascular disease straddles medicine, personalized hemodynamic modeling, machine learning, and modern imaging to help improve patient outcomes and reduce the economic impact. Hemodynamic modeling offers a non-invasive method to provide clinicians with new pre- and post- procedural metrics and aid in the selection of treatment options. Medical imaging is an integral part in clinical workflows for understanding and managing cardiac disease and interventions. Coupling machine learning with modeling, and cardiovascular imaging, provides faster modeling, improved data fidelity, and an enhanced understanding and earlier detection of cardiovascular anomalies, leading to the development of patient-specific diagnostic and predictive tools for characterizing and assessing cardiovascular outcomes. Herein, we provide a scoping review of translational hemodynamic modeling, medical imaging, and machine learning and their applications to cardiovascular interventions. We particularly focus on providing an intuitive understanding of each of these approaches and their ability to support decision making during important clinical milestones.

Hypertension, or elevated blood pressure (BP), is a marker for many cardiovascular diseases and can lead to life threatening conditions such as heart failure, coronary artery disease and stroke. Several techniques have recently been proposed and investigated for non-invasive BP monitoring. The increasing desire for telemonitoring solutions that allow patients to manage their own conditions from home has accelerated the development of new BP monitoring techniques. In this review, we present the recent progress in non-invasive blood pressure monitoring solutions emphasizing clinical validation and trade-offs between available techniques. We introduce the current BP measurement techniques with their underlying operating principles. New promising proof-of-concept studies are presented and recent modeling and machine learning approaches for improved BP estimation are summarized. This aids discussions on how new BP monitors should evaluated in order to bring forth new home monitoring solutions in wearable form factor. Finally, we discuss on unresolved challenges in making convenient, reliable and validated BP monitoring solutions.