The rapid advancements in machine learning, graphics processing technologies and the availability of medical imaging data have led to a rapid increase in the use of deep learning models in the medical domain. This was exacerbated by the rapid advancements in convolutional neural network (CNN) based architectures, which were adopted by the medical imaging community to assist clinicians in disease diagnosis. Since the grand success of AlexNet in 2012, CNNs have been increasingly used in medical image analysis to improve the efficiency of human clinicians. In recent years, three-dimensional (3D) CNNs have been employed for the analysis of medical images. In this paper, we trace the history of how the 3D CNN was developed from its machine learning roots, we provide a brief mathematical description of 3D CNN and provide the preprocessing steps required for medical images before feeding them to 3D CNNs. We review the significant research in the field of 3D medical imaging analysis using 3D CNNs (and its variants) in different medical areas such as classification, segmentation, detection and localization. We conclude by discussing the challenges associated with the use of 3D CNNs in the medical imaging domain (and the use of deep learning models in general) and possible future trends in the field.
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；University of Cincinnati（辛辛那提大学）
Image registration is a critical component in the applications of various medical image analyses. In recent years, there has been a tremendous surge in the development of deep learning (DL)-based medical image registration models. This paper provides a comprehensive review of medical image registration. Firstly, a discussion is provided for supervised registration categories, for example, fully supervised, dual supervised, and weakly supervised registration. Next, similarity-based as well as generative adversarial network (GAN)-based registration are presented as part of unsupervised registrat…
This paper discusses current methods and trends for 3D bounding box detection in volumetric medical image data. For this purpose, an overview of relevant papers from recent years is given. 2D and 3D implementations are discussed and compared. Multiple identified approaches for localizing anatomical structures are presented. The results show that most research recently focuses on Deep Learning methods, such as Convolutional Neural Networks vs. methods with manual feature engineering, e.g. Random-Regression-Forests. An overview of bounding box detection options is presented and helps researchers…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Arkansas（阿肯色大学）；University of Houston（休斯顿大学）；INRIA, Sophia Antipolis-Mediterranean Centre（法国国家信息与自动化研究所索菲亚·安蒂波利斯地中海中心）
Deep reinforcement learning (DRL) augments the reinforcement learning framework, which learns a sequence of actions that maximizes the expected reward, with the representative power of deep neural networks. Recent works have demonstrated the great potential of DRL in medicine and healthcare. This paper presents a literature review of DRL in medical imaging. We start with a comprehensive tutorial of DRL, including the latest model-free and model-based algorithms. We then cover existing DRL applications for medical imaging, which are roughly divided into three main categories: (I) parametric med…
Emory University（埃默里大学）；Winship Cancer Institute（温希普癌症研究所）；Emory University School of Medicine（埃默里大学医学院）
Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis. This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications. Specifically, we categorize the AI-based methods by training strategy: supervised and unsupervised. A detailed review of each category is presented, highlighting important contributions and achievements. Specific challenges and potential AI applications in t…
From CNN to Transformer: A Review of Medical Image Segmentation Models
University of Electronic Science and Technology of China（电子科技大学）；Deyang People’s Hospital（德阳市人民医院）；Sichuan Provincial People’s Hospital（四川省人民医院）；Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital（中国科学院四川转化医学研究医院）
A Review of Uncertainty Estimation and its Application in Medical Imaging
Sichuan University（四川大学）；Tianjin University（天津大学）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所（IHPC））
Transformer, a model comprising attention-based encoder-decoder architecture, have gained prevalence in the field of natural language processing (NLP) and recently influenced the computer vision (CV) space. The similarities between computer vision and medical imaging, reviewed the question among researchers if the impact of transformers on computer vision be translated to medical imaging? In this paper, we attempt to provide a comprehensive and recent review on the application of transformers in medical imaging by; describing the transformer model comparing it with a diversity of convolutional…
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods
Saw Swee Hock School of Public Health, National University of Singapore（新加坡国立大学苏瑞福公共卫生学院）；University of Rouen Normandy（鲁昂诺曼底大学）；Institute of Data Science, National University of Singapore（新加坡国立大学数据科学研究所）
Thomas Jefferson University Hospital（托马斯·杰斐逊大学医院）；Sidney Kimmel Jefferson Medical College（西德尼·金梅尔杰斐逊医学院）
The discussions around Artificial Intelligence (AI) and medical imaging are centered around the success of deep learning algorithms. As new algorithms enter the market, it is important for practicing radiologists to understand the pitfalls of various AI algorithms. This entails having a basic understanding of how algorithms are developed, the kind of data they are trained on, and the settings in which they will be deployed. As with all new technologies, use of AI should be preceded by a fundamental understanding of the risks and benefits to those it is intended to help. This case-based review …
Medical Image Registration and Its Application in Retinal Images: A Review
Southern University of Science and Technology（南方科技大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Singapore Eye Research Institute（新加坡眼科研究所）
Nanjing University（南京大学）；National Institute of Healthcare Data Science at Nanjing University（南京大学国家医疗健康数据科学研究院）；Istanbul Technical University（伊斯坦布尔理工大学）；University of Dundee（邓迪大学）；ShanghaiTech University（上海科技大学）；Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能有限公司）
Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review vario…
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Inserm（法国国家健康与医学研究院）；Pixyl Research and Development Laboratory（Pixyl研发实验室）；Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔理工学院）
The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associate…
Transparency in Machine Learning (ML), attempts to reveal the working mechanisms of complex models. Transparent ML promises to advance human factors engineering goals of human-centered AI in the target users. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e. a relationship between algorithm and user; as a result, iterative prototyping and evaluation with users is critical to attaining adequate solutions that afford transparency. However, following human-centered design principles in healthcare and medical image analysis is challeng…
Santa Clara University（圣克拉拉大学）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；University of Utah（犹他大学）
Objective: Employing transfer learning (TL) with convolutional neural networks (CNNs), well-trained on non-medical ImageNet dataset, has shown promising results for medical image analysis in recent years. We aimed to conduct a scoping review to identify these studies and summarize their characteristics in terms of the problem description, input, methodology, and outcome. Materials and Methods: To identify relevant studies, MEDLINE, IEEE, and ACM digital library were searched. Two investigators independently reviewed articles to determine eligibility and to extract data according to a study pro…
Imperial College London（帝国理工学院）；TU München（慕尼黑工业大学）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area. However, machine learning for medical image analysis is particularly vulnerable to natural biases like domain shifts that affect algorithmic performance and robustness. In this paper we analyze machine learning for medical image analysis within the framework of Technology Readiness Levels and review how causal analysis methods can fill a gap when creating robust…
Bangladesh University of Engineering and Technology（孟加拉国工程技术大学）；University of Cincinnati College of Medicine（辛辛那提大学医学院）；University of Cincinnati（辛辛那提大学）
The outbreak of novel coronavirus disease (COVID- 19) has claimed millions of lives and has affected all aspects of human life. This paper focuses on the application of deep learning (DL) models to medical imaging and drug discovery for managing COVID-19 disease. In this article, we detail various medical imaging-based studies such as X-rays and computed tomography (CT) images along with DL methods for classifying COVID-19 affected versus pneumonia. The applications of DL techniques to medical images are further described in terms of image localization, segmentation, registration, and classifi…
Medical imaging is crucial in modern clinics to guide the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at minimal cost and risk to the patients. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision, have been playing a prominent role. Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed, and we shall c…
Beihang University（北京航空航天大学）；The University of Melbourne（墨尔本大学）；National Institute of Informatics（国立信息学研究所）；Shanghai Jiao Tong University（上海交通大学）；Chinese Academy of Sciences（中国科学院）
Deep neural networks (DNNs) have become popular for medical image analysis tasks like cancer diagnosis and lesion detection. However, a recent study demonstrates that medical deep learning systems can be compromised by carefully-engineered adversarial examples/attacks with small imperceptible perturbations. This raises safety concerns about the deployment of these systems in clinical settings. In this paper, we provide a deeper understanding of adversarial examples in the context of medical images. We find that medical DNN models can be more vulnerable to adversarial attacks compared to models…
Interpretation of medical images for diagnosis and treatment of complex disease from high-dimensional and heterogeneous data remains a key challenge in transforming healthcare. In the last few years, both supervised and unsupervised deep learning achieved promising results in the area of medical imaging and image analysis. Unlike supervised learning which is biased towards how it is being supervised and manual efforts to create class label for the algorithm, unsupervised learning derive insights directly from the data itself, group the data and help to make data driven decisions without any ex…
Chinese Academy of Sciences（中国科学院）；Johns Hopkins Medical Institutes（约翰斯·霍普金斯医学院）；Vanderbilt University（范德堡大学）；University of Science and Technology of China（中国科学技术大学）
Transformer, the latest technological advance of deep learning, has gained prevalence in natural language processing or computer vision. Since medical imaging bear some resemblance to computer vision, it is natural to inquire about the status quo of Transformers in medical imaging and ask the question: can the Transformer models transform medical imaging? In this paper, we attempt to make a response to the inquiry. After a brief introduction of the fundamentals of Transformers, especially in comparison with convolutional neural networks (CNNs), and highlighting key defining properties that cha…
University of Waterloo（滑铁卢大学）；National Research Council Canada（加拿大国家研究委员会）
Computer vision and machine learning are playing an increasingly important role in computer-assisted diagnosis; however, the application of deep learning to medical imaging has challenges in data availability and data imbalance, and it is especially important that models for medical imaging are built to be trustworthy. Therefore, we propose TRUDLMIA, a trustworthy deep learning framework for medical image analysis, which adopts a modular design, leverages self-supervised pre-training, and utilizes a novel surrogate loss function. Experimental evaluations indicate that models generated from the…
Maastricht University（马斯特里赫特大学）；Maastricht University Medical Centre+（马斯特里赫特大学医学中心+）
Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision making process. Therefore, the…
Physics-Inspired Generative Models in Medical Imaging: A Review
KTH Royal Institute of Technology（瑞典皇家理工学院）；Karolinska University Hospital（卡罗林斯卡大学医院）；University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学MEVIS研究所）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Deep learning methods have been very effective for a variety of medical diagnostic tasks and has even beaten human experts on some of those. However, the black-box nature of the algorithms has restricted clinical use. Recent explainability studies aim to show the features that influence the decision of a model the most. The majority of literature reviews of this area have focused on taxonomy, ethics, and the need for explanations. A review of the current applications of explainable deep learning for different medical imaging tasks is presented here. The various approaches, challenges for clini…
Boston Children’s Hospital（波士顿儿童医院）；Harvard Medical School（哈佛医学院）
Supervised training of deep learning models requires large labeled datasets. There is a growing interest in obtaining such datasets for medical image analysis applications. However, the impact of label noise has not received sufficient attention. Recent studies have shown that label noise can significantly impact the performance of deep learning models in many machine learning and computer vision applications. This is especially concerning for medical applications, where datasets are typically small, labeling requires domain expertise and suffers from high inter- and intra-observer variability…
The fusion techniques that utilize multiple feature sets to form new features that are often more robust and contain useful information for future processing are referred to as feature fusion. The term data fusion is applied to the class of techniques used for combining decisions obtained from multiple feature sets to form global decisions. Feature and data fusion interchangeably represent two important classes of techniques that have proved to be of practical importance in a wide range of medical imaging problems
Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy
Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；University Hospital Essen (AöR)（埃森大学医院）；German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Imaging（亥姆霍兹成像机构）
When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis
Macquarie University（麦考瑞大学）；Macquarie Medical School（麦考瑞医学院）；Faculty of Medicine, Health and Human Sciences（医学与健康科学学院）；School of Computing（计算机学院）；Faculty of Science and Engineering（科学与工程学院）；Centre for Health Informatics（健康信息学中心）；Australian Institute of Health Innovation（澳大利亚健康创新研究院）
Imperial College London（帝国理工学院）；Technical University of Munich（慕尼黑工业大学）
In recent years, the research landscape of machine learning in medical imaging has changed drastically from supervised to semi-, weakly- or unsupervised methods. This is mainly due to the fact that ground-truth labels are time-consuming and expensive to obtain manually. Generating labels from patient metadata might be feasible but it suffers from user-originated errors which introduce biases. In this work, we propose an unsupervised approach for automatically clustering and categorizing large-scale medical image datasets, with a focus on cardiac MR images, and without using any labels. We inve…
Emory University（埃默里大学）；Winship Cancer Institute（温希普癌症研究所）；School of Medicine, Emory University（埃默里大学医学院）
Motivation: Medical image analysis involves tasks to assist physicians in qualitative and quantitative analysis of lesions or anatomical structures, significantly improving the accuracy and reliability of diagnosis and prognosis. Traditionally, these tasks are finished by physicians or medical physicists and lead to two major problems: (i) low efficiency; (ii) biased by personal experience. In the past decade, many machine learning methods have been applied to accelerate and automate the image analysis process. Compared to the enormous deployments of supervised and unsupervised learning models…
A Systematic Review of Low-Rank and Local Low-Rank Matrix Approximation in Big Data Medical Imaging
Rhodes University（罗德斯大学）；Osnabrück University（奥斯纳布吕克大学）；International Centre for Radio Astronomy Research (ICRAR)（国际射电天文研究中心）；Curtin University（科廷大学）；ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)（ARC全天天体物理学卓越中心（ASTRO 3D））；Curtin Institute of Radio Astronomy（科廷射电天文研究所）
This compendium gathers all the accepted extended abstracts from the Second International Conference on Medical Imaging with Deep Learning (MIDL 2019), held in London, UK, 8-10 July 2019. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2019 Full Paper Track are published as Volume 102 of the Proceedings of Machine Learning Research (PMLR) http://proceedings.mlr.press/v102/.
Shanghai United Imaging Intelligence Co., Ltd.（上海联影智能医疗科技有限公司）；Shanghai University（上海大学）；United Imaging Intelligence（联影智能）；Shanghai Jiao Tong University（上海交通大学）；Beihang University（北京航空航天大学）；Medical School of Nanjing University（南京大学医学院）；National Institute of Healthcare Data Science at Nanjing University（南京大学国家医疗数据科学研究院）
(This paper was submitted as an invited paper to IEEE Reviews in Biomedical Engineering on April 6, 2020.) The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered i…
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；Utrecht University（乌得勒支大学）
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis. This survey presents an overview of eXplainable Artificial Intelligence (XAI) used in deep learning-based medical image analysis. A framework of XAI criteria is introduced to classify deep learning-based medical image analysis methods. Papers on XAI techniques in medical image analysis are then surveyed and categorized according to the framework and according to anatomical location. The paper concludes with an outloo…
In this article, we perform a review of the state-of-the-art of hybrid machine learning in medical imaging. We start with a short summary of the general developments of the past in machine learning and how general and specialized approaches have been in competition in the past decades. A particular focus will be the theoretical and experimental evidence pro and contra hybrid modelling. Next, we inspect several new developments regarding hybrid machine learning with a particular focus on so-called known operator learning and how hybrid approaches gain more and more momentum across essentially a…
University of Engineering and Technology Taxila（塔克西拉工程技术大学）；University of Surrey（萨里大学）；King Abdul Aziz University（阿卜杜勒·阿齐兹国王大学）；King Saud University（沙特国王大学）
The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an effective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering has made medical image analysis one of the top research and development area. One of the reason for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of au…
Shahid Beheshti University（沙希德·贝赫什提大学）；Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；University of Leeds（利兹大学）；Royal Adelaide Hospital（阿德莱德皇家医院）；The University of Adelaide（阿德莱德大学）；Tehran University of Medical Sciences（德黑兰医科大学）
Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. Sometimes the symptoms of the disease increase so much they lead to the death of the patients. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. Many studies have tried to use medical imaging for early diagnosis of COVID-19. This study attempts to review papers on automatic methods for medical i…
College of Information Sciences and Technology, Northwest University（西北大学信息科学与技术学院）
Machine learning (ML) has seen enormous consideration during the most recent decade. This success started in 2012 when an ML model accomplished a remarkable triumph in the ImageNet Classification, the world's most famous competition for computer vision. This model was a kind of convolutional neural system (CNN) called deep learning (DL). Since then, researchers have started to participate efficiently in DL's fastest developing area of research. These days, DL systems are cutting-edge ML systems spanning a broad range of disciplines, from human language processing to video analysis, and commonl…
TU Darmstadt（达姆施塔特工业大学）；TU Munich（慕尼黑工业大学）；Radboud University Medical Center（拉德堡德大学医学中心）
Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, th…
Foundation Models in Medical Imaging: A Review and Outlook
Netherlands Cancer Institute（荷兰癌症研究所）；Radboud University Medical Center（拉德堡德大学医学中心）；Delft University of Technology（代尔夫特理工大学）；University of Amsterdam（阿姆斯特丹大学）；Oncode Institute（Oncode研究所）
RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；University of Montreal（蒙特利尔大学）；Polytechnique Montreal（蒙特利尔综合理工学院）；Mila, Quebec AI Institute（魁北克人工智能研究所Mila）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所MEVIS）
Dealing with missing modalities in Magnetic Resonance Imaging (MRI) and overcoming their negative repercussions is considered a hurdle in biomedical imaging. The combination of a specified set of modalities, which is selected depending on the scenario and anatomical part being scanned, will provide medical practitioners with full information about the region of interest in the human body, hence the missing MRI sequences should be reimbursed. The compensation of the adverse impact of losing useful information owing to the lack of one or more modalities is a well-known challenge in the field of …
Deep neural networks (DNN) have achieved unprecedented performance in computer-vision tasks almost ubiquitously in business, technology, and science. While substantial efforts are made to engineer highly accurate architectures and provide usable model explanations, most state-of-the-art approaches are first designed for natural vision and then translated to the medical domain. This dissertation seeks to address this gap by proposing novel architectures that integrate the domain-specific constraints of medical imaging into the DNN model and explanation design.
In this extended abstract, we will present and discuss opportunities and challenges brought about by a new deep learning method by AUC maximization (aka \underline{\bf D}eep \underline{\bf A}UC \underline{\bf M}aximization or {\bf DAM}) for medical image classification. Since AUC (aka area under ROC curve) is a standard performance measure for medical image classification, hence directly optimizing AUC could achieve a better performance for learning a deep neural network than minimizing a traditional loss function (e.g., cross-entropy loss). Recently, there emerges a trend of using deep AUC ma…
A Systematic Review of Few-Shot Learning in Medical Imaging
Institute of Information Science and Technologies “Alessandro Faedo”, National Research Council of Italy (ISTI-CNR)（意大利国家研究委员会信息科学与技术研究所）；Department of Information Engineering, University of Pisa（比萨大学信息工程系）
University of Pittsburgh（匹兹堡大学）；National University of Defense Technology（国防科技大学）；University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh（匹兹堡大学医学中心匹兹堡儿童医院）；Magee-Womens Hospital of University of Pittsburgh Medical Center（匹兹堡大学医学中心马吉妇女医院）
Deep learning models have gained remarkable performance on a variety of image classification tasks. However, many models suffer from limited performance in clinical or medical settings when data are imbalanced. To address this challenge, we propose a medical-knowledge-guided one-class classification approach that leverages domain-specific knowledge of classification tasks to boost the model's performance. The rationale behind our approach is that some existing prior medical knowledge can be incorporated into data-driven deep learning to facilitate model learning. We design a deep learning-base…
Guangdong University of Technology（广东工业大学）；University of Houston（休斯顿大学）；Houston Methodist Cancer Center（休斯顿卫理公会癌症中心）
Deep learning has attracted broad interest in healthcare and medical communities. However, there has been little research into the privacy issues created by deep networks trained for medical applications. Recently developed inference attack algorithms indicate that images and text records can be reconstructed by malicious parties that have the ability to query deep networks. This gives rise to the concern that medical images and electronic health records containing sensitive patient information are vulnerable to these attacks. This paper aims to attract interest from researchers in the medical…
University of Oklahoma（俄克拉荷马大学）；ShanghaiTech University（上海科技大学）；University of Oklahoma Health Sciences Center（俄克拉荷马大学健康科学中心）
Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been remarkably successful in a variety of medical imaging tasks to support disease detection and diagnosis. Despite the success, the further improvement of deep learning models in medical image analysis is majorly bottlenecked by the lack of large-sized and well-annotated datasets. In the past five years, many studies have focused on addressing this challenge. In this paper, we reviewed and summarized these recent studies to provide a comprehensive …
School of Biomedical Engineering, University of Science and Technology of China（中国科学技术大学生物医学工程学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Tel-Aviv University（特拉维夫大学）；University of Pennsylvania（宾夕法尼亚大学）；Yale University（耶鲁大学）；Radboud University Medical Center（拉德堡德大学医学中心）；Case Western Reserve University（凯斯西储大学）；Louis Stokes Cleveland Veterans Administration Medical Center（路易斯·斯托克斯克利夫兰退伍军人事务医疗中心）；Johns Hopkins University（约翰霍普金斯大学）；Klinikum rechts der Isar, TU Munich（慕尼黑工业大学右岸医院）；Imperial College（帝国理工学院）；National Institute of Health Clinical Center（美国国立卫生研究院临床中心）
Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs a…
