MBZ University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；Monash University（蒙纳士大学）；Australian National University（澳大利亚国立大学）；Linköping University（林雪平大学）；Agency for Science, Technology and Research（新加坡科技研究局）
Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, Huazhu Fu
Following unprecedented success on the natural language tasks, Transformers have been successfully applied to several computer vision problems, achieving state-of-the-art results and prompting researchers to reconsider the supremacy of convolutional neural networks (CNNs) as {de facto} operators. Capitalizing on these advances in computer vision, the medical imaging field has also witnessed growing interest for Transformers that can capture global context compared to CNNs with local receptive fields. Inspired from this transition, in this survey, we attempt to provide a comprehensive review of the applications of Transformers in medical imaging covering various aspects, ranging from recently proposed architectural designs to unsolved issues. Specifically, we survey the use of Transformers in medical image segmentation, detection, classification, reconstruction, synthesis, registration, clinical report generation, and other tasks. In particular, for each of these applications, we develop taxonomy, identify application-specific challenges as well as provide insights to solve them, and highlight recent trends. Further, we provide a critical discussion of the field's current state as a whole, including the identification of key challenges, open problems, and outlining promising future directions. We hope this survey will ignite further interest in the community and provide researchers with an up-to-date reference regarding applications of Transformer models in medical imaging. Finally, to cope with the rapid development in this field, we intend to regularly update the relevant latest papers and their open-source implementations at \url{https://github.com/fahadshamshad/awesome-transformers-in-medical-imaging}.
The Athenian School（雅典纳中学）；Cornell University（康奈尔大学）
Because of the necessity to obtain high-quality images with minimal radiation doses, such as in low-field magnetic resonance imaging, super-resolution reconstruction in medical imaging has become more popular (MRI). However, due to the complexity and high aesthetic requirements of medical imaging, image super-resolution reconstruction remains a difficult challenge. In this paper, we offer a deep learning-based strategy for reconstructing medical images from low resolutions utilizing Transformer and Generative Adversarial Networks (T-GAN). The integrated system can extract more precise texture …
Istanbul Technical University（伊斯坦布尔技术大学）；Mehmet Akif Ersoy Thoracic and Cardiovascular Surgery Training and Research Hospital（穆罕默德·阿基夫·埃尔索伊胸心血管外科培训研究医院）；Acibadem University（阿哲巴登大学）；King’s College London（伦敦国王学院）
To maintain a standard in a medical imaging study, images should have necessary image quality for potential diagnostic use. Although CNN-based approaches are used to assess the image quality, their performance can still be improved in terms of accuracy. In this work, we approach this problem by using Swin Transformer, which improves the poor-quality image classification performance that causes the degradation in medical image quality. We test our approach on Foreign Object Classification problem on Chest X-Rays (Object-CXR) and Left Ventricular Outflow Tract Classification problem on Cardiac M…
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.
Bilkent University（比尔肯特大学）；National Magnetic Resonance Research Center (UMRAM)（国家磁共振研究中心（UMRAM））；Sabuncu Brain Research Center（萨本库脑研究中心）
Generative adversarial models with convolutional neural network (CNN) backbones have recently been established as state-of-the-art in numerous medical image synthesis tasks. However, CNNs are designed to perform local processing with compact filters, and this inductive bias compromises learning of contextual features. Here, we propose a novel generative adversarial approach for medical image synthesis, ResViT, that leverages the contextual sensitivity of vision transformers along with the precision of convolution operators and realism of adversarial learning.} ResViT's generator employs a cent…
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））
Rethinking Attention Gated with Hybrid Dual Pyramid Transformer-CNN for Generalized Segmentation in Medical Imaging
Junia（朱尼亚学院）；Centrale Lille（里尔中央理工学院）；University of the Basque Country UPV/EHU（巴斯克大学 UPV/EHU）；Université Polytechnique Hauts-de-France（上法兰西理工大学）；Université de Lille（里尔大学）；Ho Chi Minh City Open University（胡志明市开放大学）
Nagoya Institute of Technology（名古屋工业大学）；RIKEN Center for Advanced Intelligence Project（理化学研究所先进智能项目中心）；Kurume University School of Medicine（久留米大学医学院）；Nagoya University（名古屋大学）
In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based not only on the images but also on a variety of clinical records. This means that pathologists examine medical images with some prior knowledge of the patients and that the attention regions may change depending on the clinical records. In this study, we propose a method called the Pe…
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…
Institute of High Performance Computing, A*STAR（新加坡科技研究局高性能计算研究所）；University of Electronic Science and Technology of China（电子科技大学）
Medical image segmentation is important for computer-aided diagnosis. Good segmentation demands the model to see the big picture and fine details simultaneously, i.e., to learn image features that incorporate large context while keep high spatial resolutions. To approach this goal, the most widely used methods -- U-Net and variants, extract and fuse multi-scale features. However, the fused features still have small "effective receptive fields" with a focus on local image cues, limiting their performance. In this work, we propose Segtran, an alternative segmentation framework based on transform…
University of Central Florida（中佛罗里达大学）；Northwestern University（西北大学）；Mayo Clinic（梅奥诊所）；Sheikh Shakhbout Medical City（谢赫·沙赫布特医疗城）
Current data augmentation techniques and transformations are well suited for improving the size and quality of natural image datasets but are not yet optimized for medical imaging. We hypothesize that sub-optimal data augmentations can easily distort or occlude medical images, leading to false positives or negatives during patient diagnosis, prediction, or therapy/surgery evaluation. In our experimental results, we found that utilizing commonly used intensity-based data augmentation distorts the MRI scans and leads to texture information loss, thus negatively affecting the overall performance …
A generalizable 3D framework and model for self-supervised learning in medical imaging
Department of Medical Biophysics, University of Toronto（多伦多大学医学生物物理学系）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Institute for Aerospace Studies, University of Toronto（多伦多大学航空航天研究所）；Physical Sciences Platform, Sunnybrook Research Institute（圣母医院研究学院物理科学平台）；Vector Institute, Toronto（多伦多向量研究所）；Department of Laboratory Medicine and Pathobiology, University of Toronto（多伦多大学实验室医学与病理学系）；Hurvitz Brain Sciences, Sunnybrook Health Sciences Centre（圣母医院健康科学中心Hurvitz脑科学）；Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre（圣母医院健康科学中心Harquail神经调制中心）
Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection. CNN has great advantages in extracting local features of images. However, due to the locality of convolution operation, it can not deal with long-range relationships well. Recently, transformers have been applied to computer vision and achieved remarkable success in large-scale datasets. Compared with natural images, multi-modal medical images have explicit and important long-range dependenci…
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…
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…
National University of Sciences and Technology (NUST)（国立科技大学（NUST））
Recent escalation in the field of computer vision underpins a huddle of algorithms with the magnificent potential to unravel the information contained within images. These computer vision algorithms are being practised in medical image analysis and are transfiguring the perception and interpretation of Imaging data. Among these algorithms, Vision Transformers are evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by a plenty of researchers to perform new as well as former experiments. Here, in thi…
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…
Johns Hopkins Medical Institutes（约翰·霍普金斯医学院）；Johns Hopkins University（约翰·霍普金斯大学）；NVIDIA Corporation（英伟达公司）；Duke University Medical Center（杜克大学医学中心）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
In the last decade, convolutional neural networks (ConvNets) have been a major focus of research in medical image analysis. However, the performances of ConvNets may be limited by a lack of explicit consideration of the long-range spatial relationships in an image. Recently Vision Transformer architectures have been proposed to address the shortcomings of ConvNets and have produced state-of-the-art performances in many medical imaging applications. Transformers may be a strong candidate for image registration because their substantially larger receptive field enables a more precise comprehensi…
Given the prevalence of 3D medical imaging technologies such as MRI and CT that are widely used in diagnosing and treating diverse diseases, 3D segmentation is one of the fundamental tasks of medical image analysis. Recently, Transformer-based models have started to achieve state-of-the-art performances across many vision tasks, through pre-training on large-scale natural image benchmark datasets. While works on medical image analysis have also begun to explore Transformer-based models, there is currently no optimal strategy to effectively leverage pre-trained Transformers, primarily due to th…
Jordan University of Science and Technology（约旦科技大学）
The scarcity of high-quality annotated medical imaging datasets is a major problem that collides with machine learning applications in the field of medical imaging analysis and impedes its advancement. Self-supervised learning is a recent training paradigm that enables learning robust representations without the need for human annotation which can be considered an effective solution for the scarcity of annotated medical data. This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with a concentration on their applications in the fiel…
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…
While deep learning methods have shown great success in medical image analysis, they require a number of medical images to train. Due to data privacy concerns and unavailability of medical annotators, it is oftentimes very difficult to obtain a lot of labeled medical images for model training. In this paper, we study cross-modality data augmentation to mitigate the data deficiency issue in the medical imaging domain. We propose a discriminative unpaired image-to-image translation model which translates images in source modality into images in target modality where the translation task is condu…
Medical imaging AI systems such as disease classification and segmentation are increasingly inspired and transformed from computer vision based AI systems. Although an array of adversarial training and/or loss function based defense techniques have been developed and proved to be effective in computer vision, defending against adversarial attacks on medical images remains largely an uncharted territory due to the following unique challenges: 1) label scarcity in medical images significantly limits adversarial generalizability of the AI system; 2) vastly similar and dominant fore- and backgroun…
This work provides a strong baseline for the problem of multi-source multi-target domain adaptation and generalization in medical imaging. Using a diverse collection of ten chest X-ray datasets, we empirically demonstrate the benefits of training medical imaging deep learning models on varied patient populations for generalization to out-of-sample domains.
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）
MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Heidelberg University（海德堡大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心）；University Medical Center Heidelberg（海德堡大学医院）；Helmholtz Imaging（亥姆霍兹成像机构）
Transformer has achieved impressive successes for various computer vision tasks. However, most of existing studies require to pretrain the Transformer backbone on a large-scale labeled dataset (e.g., ImageNet) for achieving satisfactory performance, which is usually unavailable for medical images. Additionally, due to the gap between medical and natural images, the improvement generated by the ImageNet pretrained weights significantly degrades while transferring the weights to medical image processing tasks. In this paper, we propose Bootstrap Own Latent of Transformer (BOLT), a self-supervise…
AstraZeneca（阿斯利康）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Science for Life Laboratory（生命科学实验室）
Convolutional Neural Networks (CNNs) have reigned for a decade as the de facto approach to automated medical image diagnosis. Recently, vision transformers (ViTs) have appeared as a competitive alternative to CNNs, yielding similar levels of performance while possessing several interesting properties that could prove beneficial for medical imaging tasks. In this work, we explore whether it is time to move to transformer-based models or if we should keep working with CNNs - can we trivially switch to transformers? If so, what are the advantages and drawbacks of switching to ViTs for medical ima…
Recent work has investigated the distributions of learned convolution filters through a large-scale study containing hundreds of heterogeneous image models. Surprisingly, on average, the distributions only show minor drifts in comparisons of various studied dimensions including the learned task, image domain, or dataset. However, among the studied image domains, medical imaging models appeared to show significant outliers through "spikey" distributions, and, therefore, learn clusters of highly specific filters different from other domains. Following this observation, we study the collected med…
University of Hawaii（夏威夷大学）；University of Hawaii Cancer Center（夏威夷大学癌症中心）；Pennington Biomedical Research Center（彭宁顿生物医学研究中心）；Louisiana State University（路易斯安那州立大学）
Fundamental differences between natural and medical images have recently favored the use of self-supervised learning (SSL) over ImageNet transfer learning for medical image applications. Differences between image types are primarily due to the imaging modality and medical images utilize a wide range of physics based techniques while natural images are captured using only visible light. While many have demonstrated that SSL on medical images has resulted in better downstream task performance, our work suggests that more performance can be gained. The scientific principles which are used to acqu…
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/.
Imperial College London（帝国理工学院）；Technische Universität München（慕尼黑工业大学）；FAU Erlangen–Nürnberg（埃尔朗根-纽伦堡大学）；National Institutes of Health（美国国立卫生研究院）
Chest radiographs are one of the most common diagnostic modalities in clinical routine. It can be done cheaply, requires minimal equipment, and the image can be diagnosed by every radiologists. However, the number of chest radiographs obtained on a daily basis can easily overwhelm the available clinical capacities. We propose RATCHET: RAdiological Text Captioning for Human Examined Thoraces. RATCHET is a CNN-RNN-based medical transformer that is trained end-to-end. It is capable of extracting image features from chest radiographs, and generates medically accurate text reports that fit seamless…
Microsoft Health and Life Sciences（微软健康与生命科学部）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福医学与成像人工智能中心）
Clinical Artificial lntelligence (AI) applications are rapidly expanding worldwide, and have the potential to impact to all areas of medical practice. Medical imaging applications constitute a vast majority of approved clinical AI applications. Though healthcare systems are eager to adopt AI solutions a fundamental question remains: \textit{what happens after the AI model goes into production?} We use the CheXpert and PadChest public datasets to build and test a medical imaging AI drift monitoring workflow to track data and model drift without contemporaneous ground truth. We simulate drift in…
Lesion segmentation in medical imaging has been an important topic in clinical research. Researchers have proposed various detection and segmentation algorithms to address this task. Recently, deep learning-based approaches have significantly improved the performance over conventional methods. However, most state-of-the-art deep learning methods require the manual design of multiple network components and training strategies. In this paper, we propose a new automated machine learning algorithm, T-AutoML, which not only searches for the best neural architecture, but also finds the best combinat…
Acquiring high-quality annotations in medical imaging is usually a costly process. Automatic label extraction with natural language processing (NLP) has emerged as a promising workaround to bypass the need of expert annotation. Despite the convenience, the limitation of such an approximation has not been carefully examined and is not well understood. With a challenging set of 1,000 chest X-ray studies and their corresponding radiology reports, we show that there exists a surprisingly large discrepancy between what radiologists visually perceive and what they clinically report. Furthermore, wit…
Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review
University of Regensburg（雷根斯堡大学）；Fraunhofer Institute for Digital Medicine MEVIS（弗劳恩霍夫数字医学研究所）；RWTH Aachen University（亚琛工业大学）；Iran University of Science and Technology（伊朗科技大学）；Shahid Beheshti University（沙希德·贝赫什提大学）
Digital medical images are always displayed scaled to fit particular view. Interpolation is responsible for this scaling, and if not done properly, can significantly degrade diagnostic image quality. However, theoretically-optimal interpolation algorithms may also be the most time-consuming and impractical. We propose a new approach, adapted to the needs of digital medical imaging, to combine high interpolation speed and superior L2-optimal image quality.
Transfer learning is a standard technique to improve performance on tasks with limited data. However, for medical imaging, the value of transfer learning is less clear. This is likely due to the large domain mismatch between the usual natural-image pre-training (e.g. ImageNet) and medical images. However, recent advances in transfer learning have shown substantial improvements from scale. We investigate whether modern methods can change the fortune of transfer learning for medical imaging. For this, we study the class of large-scale pre-trained networks presented by Kolesnikov et al. on three …
Zhejiang University（浙江大学）；University of Notre Dame（圣母大学）；Gachon University（嘉泉大学）；Shanghai University（上海大学）
Computer-aided medical image segmentation has been applied widely in diagnosis and treatment to obtain clinically useful information of shapes and volumes of target organs and tissues. In the past several years, convolutional neural network (CNN) based methods (e.g., U-Net) have dominated this area, but still suffered from inadequate long-range information capturing. Hence, recent work presented computer vision Transformer variants for medical image segmentation tasks and obtained promising performances. Such Transformers model long-range dependency by computing pair-wise patch relations. Howe…
Iran University of Science and Technology（伊朗科技大学）；West Virginia University（西弗吉尼亚大学）
Convolutional Neural Networks (CNNs) have advanced existing medical systems for automatic disease diagnosis. However, there are still concerns about the reliability of deep medical diagnosis systems against the potential threats of adversarial attacks since inaccurate diagnosis could lead to disastrous consequences in the safety realm. In this study, we propose a highly robust yet efficient CNN-Transformer hybrid model which is equipped with the locality of CNNs as well as the global connectivity of vision Transformers. To mitigate the high quadratic complexity of the self-attention mechanism …
Data Science Group, Faculty of Science Radboud University, Nijmegen, the Netherlands（拉德堡德大学理学院数据科学组）
Multi-view medical image analysis often depends on the combination of information from multiple views. However, differences in perspective or other forms of misalignment can make it difficult to combine views effectively, as registration is not always possible. Without registration, views can only be combined at a global feature level, by joining feature vectors after global pooling. We present a novel cross-view transformer method to transfer information between unregistered views at the level of spatial feature maps. We demonstrate this method on multi-view mammography and chest X-ray datase…
In this paper we broadly consider techniques which utilize projections on rays for data collection, with particular emphasis on optical techniques. We formulate a variety of imaging techniques as either special cases or extensions of tomographic reconstruction. We then consider how the techniques must be extended to describe objects containing occlusion, as with a self-occluding opaque object. We formulate the reconstruction problem as a regularized nonlinear optimization problem to simultaneously solve for object brightness and attenuation, where the attenuation can become infinite. We demons…
Sun Yat-sen University（中山大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Xi’an Jiaotong University（西安交通大学）；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院北京协和医院）；University of Portsmouth（朴茨茅斯大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）；Qingdao Academy of Intelligent Industries（青岛智能产业技术研究院）
There has been much progress in data-driven artificial intelligence technology for medical image analysis in the last decades. However, it still remains challenging due to its distinctive complexity of acquiring and annotating image data, extracting medical domain knowledge, and explaining the diagnostic decision for medical image analysis. In this paper, we propose a data-knowledge-driven framework termed as Parallel Medical Imaging (PMI) for intelligent medical image analysis based on the methodology of interactive ACP-based parallel intelligence. In the PMI framework, computational experime…
