IEEE Reviews in Biomedical Engineering最新文献_第3页

Vision Transformers for Computational Histopathology 用于计算组织病理学的视觉转换器

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-07-21 DOI: 10.1109/RBME.2023.3297604

Hongming Xu;Qi Xu;Fengyu Cong;Jeonghyun Kang;Chu Han;Zaiyi Liu;Anant Madabhushi;Cheng Lu

{"title":"Vision Transformers for Computational Histopathology","authors":"Hongming Xu;Qi Xu;Fengyu Cong;Jeonghyun Kang;Chu Han;Zaiyi Liu;Anant Madabhushi;Cheng Lu","doi":"10.1109/RBME.2023.3297604","DOIUrl":"10.1109/RBME.2023.3297604","url":null,"abstract":"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.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10227147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Beyond Supervised Learning for Pervasive Healthcare 超越监督学习，实现无处不在的医疗保健。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-07-20 DOI: 10.1109/RBME.2023.3296938

Xiao Gu;Fani Deligianni;Jinpei Han;Xiangyu Liu;Wei Chen;Guang-Zhong Yang;Benny Lo

{"title":"Beyond Supervised Learning for Pervasive Healthcare","authors":"Xiao Gu;Fani Deligianni;Jinpei Han;Xiangyu Liu;Wei Chen;Guang-Zhong Yang;Benny Lo","doi":"10.1109/RBME.2023.3296938","DOIUrl":"10.1109/RBME.2023.3296938","url":null,"abstract":"The integration of machine/deep learning and sensing technologies is transforming healthcare and medical practice. However, inherent limitations in healthcare data, namely \u0000<italic>scarcity</i>\u0000, \u0000<italic>quality</i>\u0000, and \u0000<italic>heterogeneity</i>\u0000, hinder the effectiveness of supervised learning techniques which are mainly based on pure statistical fitting between data and labels. In this article, we first identify the challenges present in machine learning for pervasive healthcare and we then review the current trends beyond fully supervised learning that are developed to address these three issues. Rooted in the inherent drawbacks of empirical risk minimization that underpins pure fully supervised learning, this survey summarizes seven key lines of learning strategies, to promote the generalization performance for real-world deployment. In addition, we point out several directions that are emerging and promising in this area, to develop data-efficient, scalable, and trustworthy computational models, and to leverage multi-modality and multi-source sensing informatics, for pervasive healthcare.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9962013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems 基于图像的食物识别和体积估算人工智能系统综述。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-06-05 DOI: 10.1109/RBME.2023.3283149

Fotios S. Konstantakopoulos;Eleni I. Georga;Dimitrios I. Fotiadis

{"title":"A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems","authors":"Fotios S. Konstantakopoulos;Eleni I. Georga;Dimitrios I. Fotiadis","doi":"10.1109/RBME.2023.3283149","DOIUrl":"10.1109/RBME.2023.3283149","url":null,"abstract":"The daily healthy diet and balanced intake of essential nutrients play an important role in modern lifestyle. The estimation of a meal's nutrient content is an integral component of significant diseases, such as diabetes, obesity and cardiovascular disease. Lately, there has been an increasing interest towards the development and utilization of smartphone applications with the aim of promoting healthy behaviours. The semi – automatic or automatic, precise and in real-time estimation of the nutrients of daily consumed meals is approached in relevant literature as a computer vision problem using food images which are taken via a user's smartphone. Herein, we present the state-of-the-art on automatic food recognition and food volume estimation methods starting from their basis, i.e., the food image databases. First, by methodically organizing the extracted information from the reviewed studies, this review study enables the comprehensive fair assessment of the methods and techniques applied for segmenting food images, classifying their food content and computing the food volume, associating their results with the characteristics of the used datasets. Second, by unbiasedly reporting the strengths and limitations of these methods and proposing pragmatic solutions to the latter, this review can inspire future directions in the field of dietary assessment systems.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10144465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9934987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review 鼻咽癌放射组学与深度学习：综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-04-25 DOI: 10.1109/RBME.2023.3269776

Zipei Wang;Mengjie Fang;Jie Zhang;Linquan Tang;Lianzhen Zhong;Hailin Li;Runnan Cao;Xun Zhao;Shengyuan Liu;Ruofan Zhang;Xuebin Xie;Haiqiang Mai;Sufang Qiu;Jie Tian;Di Dong

{"title":"Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review","authors":"Zipei Wang;Mengjie Fang;Jie Zhang;Linquan Tang;Lianzhen Zhong;Hailin Li;Runnan Cao;Xun Zhao;Shengyuan Liu;Ruofan Zhang;Xuebin Xie;Haiqiang Mai;Sufang Qiu;Jie Tian;Di Dong","doi":"10.1109/RBME.2023.3269776","DOIUrl":"10.1109/RBME.2023.3269776","url":null,"abstract":"Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9337472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Systematic Development of a Simple Human Gait Index 系统开发简单人体步态指数

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-03-24 DOI: 10.1109/RBME.2023.3279655

Abu Ilius Faisal;Tapas Mondal;M. Jamal Deen

{"title":"Systematic Development of a Simple Human Gait Index","authors":"Abu Ilius Faisal;Tapas Mondal;M. Jamal Deen","doi":"10.1109/RBME.2023.3279655","DOIUrl":"10.1109/RBME.2023.3279655","url":null,"abstract":"Human gait analysis aims to assess gait mechanics and to identify the deviations from “normal” gait patterns by using meaningful parameters extracted from gait data. As each parameter indicates different gait characteristics, a proper combination of key parameters is required to perform an overall gait assessment. Therefore, in this study, we introduced a simple gait index derived from the most important gait parameters (walking speed, maximum knee flexion angle, stride length, and stance-swing phase ratio) to quantify overall gait quality. We performed a systematic review to select the parameters and analyzed a gait dataset (120 healthy subjects) to develop the index and to determine the healthy range (0.50 – 0.67). To validate the parameter selection and to justify the defined index range, we applied a support vector machine algorithm to classify the dataset based on the selected parameters and achieved a high classification accuracy (∼95%). Also, we explored other published datasets that are in good agreement with the proposed index prediction, reinforcing the reliability and effectiveness of the developed gait index. The gait index can be used as a reference for preliminary assessment of human gait conditions and to quickly identify abnormal gait patterns and possible relation to health issues.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9517689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review 心率变异性分析中的不确定性：系统回顾

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-03-15 DOI: 10.1109/RBME.2023.3271595

Lei Lu;Tingting Zhu;Davide Morelli;Andrew Creagh;Zhangdaihong Liu;Jenny Yang;Fenglin Liu;Yuan-Ting Zhang;David A. Clifton

{"title":"Uncertainties in the Analysis of Heart Rate Variability: A Systematic Review","authors":"Lei Lu;Tingting Zhu;Davide Morelli;Andrew Creagh;Zhangdaihong Liu;Jenny Yang;Fenglin Liu;Yuan-Ting Zhang;David A. Clifton","doi":"10.1109/RBME.2023.3271595","DOIUrl":"10.1109/RBME.2023.3271595","url":null,"abstract":"Heart rate variability (HRV) is an important metric with a variety of applications in clinical situations such as cardiovascular diseases, diabetes mellitus, and mental health. HRV data can be potentially obtained from electrocardiography and photoplethysmography signals, then computational techniques such as signal filtering and data segmentation are used to process the sampled data for calculating HRV measures. However, uncertainties arising from data acquisition, computational models, and physiological factors can lead to degraded signal quality and affect HRV analysis. Therefore, it is crucial to address these uncertainties and develop advanced models for HRV analysis. Although several reviews of HRV analysis exist, they primarily focus on clinical applications, trends in HRV methods, or specific aspects of uncertainties such as measurement noise. This paper provides a comprehensive review of uncertainties in HRV analysis, quantifies their impacts, and outlines potential solutions. To the best of our knowledge, this is the first study that presents a holistic review of uncertainties in HRV methods and quantifies their impacts on HRV measures from an engineer's perspective. This review is essential for developing robust and reliable models, and could serve as a valuable future reference in the field, particularly for dealing with uncertainties in HRV analysis.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10124275","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9471426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Review of Parallel Transmit Arrays for Ultra-High Field MR Imaging 超高场磁共振成像并行传输阵列回顾。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-02-10 DOI: 10.1109/RBME.2023.3244132

Chang-Hoon Choi;Andrew Webb;Stephan Orzada;Mikheil Kelenjeridze;N. Jon Shah;Jörg Felder

{"title":"A Review of Parallel Transmit Arrays for Ultra-High Field MR Imaging","authors":"Chang-Hoon Choi;Andrew Webb;Stephan Orzada;Mikheil Kelenjeridze;N. Jon Shah;Jörg Felder","doi":"10.1109/RBME.2023.3244132","DOIUrl":"10.1109/RBME.2023.3244132","url":null,"abstract":"Parallel transmission (pTX) techniques are required to tackle a number of challenges, e.g., the inhomogeneous distribution of the transmit field and elevated specific absorption rate (SAR), in ultra-high field (UHF) MR imaging. Additionally, they offer multiple degrees of freedom to create temporally- and spatially-tailored transverse magnetization. Given the increasing availability of MRI systems at 7 T and above, it is anticipated that interest in pTX applications will grow accordingly. One of the key components in MR systems capable of pTX is the design of the transmit array, as this has a major impact on performance in terms of power requirements, SAR and RF pulse design. While several reviews on pTX pulse design and the clinical applicability of UHF exist, there is currently no systematic review of pTX transmit/transceiver coils and their associated performance. In this article, we analyze transmit array concepts to determine the strengths and weaknesses of different types of design. We systematically review the different types of individual antennas employed for UHF, their combination into pTX arrays, and methods to decouple the individual elements. We also reiterate figures-of-merit (FoMs) frequently employed to describe the performance of pTX arrays and summarize published array designs in terms of these FoMs.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9253731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes 用于血糖监测和妊娠糖尿病管理的数字健康和机器学习技术。

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-02-07 DOI: 10.1109/RBME.2023.3242261

Huiqi Y. Lu;Xiaorong Ding;Jane E. Hirst;Yang Yang;Jenny Yang;Lucy Mackillop;David A. Clifton

{"title":"Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes","authors":"Huiqi Y. Lu;Xiaorong Ding;Jane E. Hirst;Yang Yang;Jenny Yang;Lucy Mackillop;David A. Clifton","doi":"10.1109/RBME.2023.3242261","DOIUrl":"10.1109/RBME.2023.3242261","url":null,"abstract":"Innovations in digital health and machine learning are changing the path of clinical health and care. People from different geographical locations and cultural backgrounds can benefit from the mobility of wearable devices and smartphones to monitor their health ubiquitously. This paper focuses on reviewing the digital health and machine learning technologies used in gestational diabetes – a subtype of diabetes that occurs during pregnancy. This paper reviews sensor technologies used in blood glucose monitoring devices, digital health innovations and machine learning models for gestational diabetes monitoring and management, in clinical and commercial settings, and discusses future directions. Despite one in six mothers having gestational diabetes, digital health applications were underdeveloped, especially the techniques that can be deployed in clinical practice. There is an urgent need to (1) develop clinically interpretable machine learning methods for patients with gestational diabetes, assisting health professionals with treatment, monitoring, and risk stratification before, during and after their pregnancies; (2) adapt and develop clinically-proven devices for patient self-management of health and well-being at home settings (“virtual ward” and virtual consultation), thereby improving clinical outcomes by facilitating timely intervention; and (3) ensure innovations are affordable and sustainable for all women with different socioeconomic backgrounds and clinical resources.","PeriodicalId":39235,"journal":{"name":"IEEE Reviews in Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":17.6,"publicationDate":"2023-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10492955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

IEEE Engineering in Medicine and Biology Society Information IEEE医学与生物工程学会信息

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-01-05 DOI: 10.1109/RBME.2022.3228083

引用次数: 0

IEEE Reviews in Biomedical Engineering (R-BME) Information IEEE生物医学工程（R-BME）信息综述

IF 17.6 1区工程技术

IEEE Reviews in Biomedical Engineering Pub Date : 2023-01-05 DOI: 10.1109/RBME.2022.3228079

引用次数: 0