The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. We introduce a novel and broaden solution called Few/one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. We then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection/recognition systems using ZSL.
Deep Learning in Detection and Diagnosis of Covid-19 using Radiology Modalities: A Systematic Review
Shahid Beheshti University of Medical Sciences（沙希德·贝赫什提医科大学）；Student Research Committee（学生研究委员会）；School of Allied Medical Science（Allied Medical Science学院）；Department of Health Information Technology and Management（卫生信息技术与管理系）
目的：2019冠状病毒病的早期发现和诊断以及以最低成本和在疾病早期准确分离患者与非2019冠状病毒病病例是2019冠状病毒病流行的主要挑战之一。关于疾病的新颖性，基于放射图像的诊断方法尽管在诊断中心有许多用途，但仍存在不足。因此，医学和计算机研究人员倾向于使用机器学习模型来分析放射学图像。研究方法：本次系统综述通过检索PubMed、Scopus和Web of Science三个数据库进行，检索时间为2019年11月1日至2020年7月20日。基于检索策略，关键词为Covid-19、深度学习、诊断和检测，提取了168篇文章，最终通过纳入和排除标准选择37篇文章作为研究人群。测试结果：本综述研究概述了通过放射学模式检测和诊断Covid-19的所有模型的现状及其基于深度学习的处理。根据这一发现，基于深度学习的模型具有非凡的能力，可以实现准确有效的COVID-19检测和诊断系统，将其用于CT扫描和X射线图像的处理，将导致灵敏度和特异性值的显着增加。结论：深度学习（DL）在Covid-19放射图像处理领域的应用减少了该疾病检测和诊断中的假阳性和阴性错误，并为患者提供了快速，廉价和安全的诊断服务的最佳机会。
A comparison of deep machine learning algorithms in COVID-19 disease diagnosis
Dr. Babasaheb Ambedkar Technological University（巴巴萨海布·安贝德卡尔技术大学）；Chitkara University（奇卡拉大学）；Pravara Rural Engineering College（普拉瓦拉农村工程学院）；Sri Guru Granth Sahib World University（圣古鲁格兰特 sahib 世界大学）
Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
Concordia Institute for Information Systems Engineering (CIISE)（康考迪亚信息系统工程研究所）；Concordia University（康考迪亚大学）；Sunnybrook Health Sciences Centre（森尼布鲁克健康科学中心）；University of Toronto（多伦多大学）；McGill University Health Center-Research Institute（麦吉尔大学健康中心研究所）；University of Calgary（卡尔加里大学）
COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19
The University of Sydney（悉尼大学）；Shanghai Jiao Tong University（上海交通大学）；Shandong First Medical University（山东第一医科大学）；Shandong Academy of Medical Sciences（山东省医学科学院）；Shandong Cancer Hospital and Institute（山东省肿瘤医院）
Fused Deep Convolutional Neural Network for Precision Diagnosis of COVID-19 Using Chest X-Ray Images
Christian Brothers University（基督教兄弟大学）；University of Dayton（代顿大学）
随着全球冠状病毒病（COVID-19）病例数超过1000万，对诊断能力的需求增加。提高诊断能力的主要变量是降低成本、周转或诊断时间以及前期设备成本和可获得性。机器学习COVID-19诊断的两个候选方法是计算机断层扫描（CT）扫描和胸部X光平片。虽然CT扫描的灵敏度更高，但与普通胸部X射线相比，它们具有更高的成本，维护要求和周转时间。美国放射学会（ACR）推荐使用便携式胸部X射线摄影（CXR），因为使用CT会给放射学服务带来巨大负担。因此，X射线图像与机器学习技术相结合被提议作为COVID-19诊断的一线分诊工具。在本文中，我们提出了一种计算机辅助诊断（CAD），通过微调在ImageNet数据集上预训练的几个神经网络（ResNet 18，ResNet 50，DenseNet 201）来准确分类COVID-19和正常受试者的胸部X射线扫描。这些神经网络融合在一个并行的架构和投票标准的候选对象类之间的最终分类决策，其中每个神经网络的输出是代表一个单一的投票。在弱标记COVID-19-CT-CXR数据集上进行了几项实验，该数据集由从PubMed Central Open Access子集中提取的263张COVID-19 CXR图像与25张正常分类CXR图像组成。这些实验显示了一个乐观的结果和能力，该模型优于许多国家的最先进的算法在几个措施。使用k折交叉验证和装袋分类器集成，我们实现了99.…
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications
Emory School of Medicine（埃默里医学院）；MentorGraphics Indian Pvt. Ltd.（MentorGraphics印度私人有限公司）；K. N. Toosi University of Technology（K.N.托西理工大学）；EMORY Hospital（埃默里医院）
Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images
Human-level COVID-19 Diagnosis from Low-dose CT Scans Using a Two-stage Time-distributed Capsule Network
Concordia University（康考迪亚大学）；University of Toronto（多伦多大学）；McGill University Health Center-Research Institute（麦吉尔大学健康中心-研究所）；Sunnybrook Health Sciences Centre（新宁健康科学中心）；University of Montreal（蒙特利尔大学）；National Cancer Institute（国家癌症研究所）
Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus
Korea Advanced Institute of Science and Technology（韩国科学技术院）；Asan Medical Center（峨山医院）；University of Ulsan College of Medicine（蔚山大学医学院）；College of Medicine, Yeungnam University（岭南大学医学院）；School of Medicine, Kyungpook National University（庆北国立大学医学院）
New Bag of Deep Visual Words based features to classify chest x-ray images for COVID-19 diagnosis
Deakin University（迪肯大学）
由于严重急性呼吸系统综合征冠状病毒2（COVID-19）的感染会导致肺部出现肺炎样效应，因此胸部X光检查有助于诊断疾病。对于图像的自动分析，它们在机器中由一组语义特征表示。深度学习（DL）模型被广泛用于从图像中提取特征。一般的深度特征可能不适合表示胸部X射线，因为它们具有一些语义区域。尽管基于视觉词袋（BoVW）的特征被证明更适合于X射线类型的图像，但现有的BoVW特征可能无法捕获足够的信息来区分COVID-19感染与其他肺炎相关感染。在本文中，我们提出了一种新的BoVW方法，称为Bag of Deep Visual Words（BoDVW），通过删除特征图归一化步骤并在原始特征图上添加深度特征归一化步骤。这有助于保留每个特征图的语义，这些特征图可能具有区分COVID-19和肺炎的重要线索。我们评估了我们提出的BoDVW特征在使用支持向量机（SVM）诊断COVID-19的胸部X射线分类中的有效性。我们在公开的COVID-19 X射线数据集上的结果表明，与最先进的方法相比，我们的特征可以在更短的计算时间内产生稳定和突出的分类准确性，特别是将COVID-19感染与其他肺炎区分开来。因此，我们的方法可以成为大规模快速诊断COVID-19患者的非常有用的工具。
Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning
K. N. Toosi University of Technology（卡纳齐·图西科技大学）；Ferdowsi University of Mashhad（菲尔多西大学）；University of La Rioja（拉里奥哈大学）；Indian Institute of Technology Indore（印度理工学院印多尔分校）；Universidad de Granada（格拉纳达大学）
Multi-Scale Feature Fusion using Parallel-Attention Block for COVID-19 Chest X-ray Diagnosis
Rutgers Cancer Institute of New Jersey（罗格斯新泽西癌症研究所）；Rutgers Robert Wood Johnson Medical School（罗格斯罗伯特·伍德·约翰逊医学院）；The University of British Columbia（不列颠哥伦比亚大学）
CXR-Net: An Encoder-Decoder-Encoder Multitask Deep Neural Network for Explainable and Accurate Diagnosis of COVID-19 pneumonia with Chest X-ray Images
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；Whiston Hospital（惠斯顿医院）；St Helens and Knowsley Teaching Hospitals NHS Trust（圣海伦斯和诺斯利教学医院国民保健服务信托）；University of Sheffield（谢菲尔德大学）
Medical Imaging with Deep Learning for COVID- 19 Diagnosis: A Comprehensive Review
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）
Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；Asan Medical Center, University of Ulsan College of Medicine（蔚山大学医学院峨山医学中心）；College of Medicine, Yeungnam University（岭南大学医学院）；School of Medicine, Kyungpook National University（庆北国立大学医学院）
Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
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）
Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection
Sun Yat-sen University（中山大学）；Shuguang Hospital, Shanghai University of Traditional Chinese Medicine（上海中医药大学附属曙光医院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Uncertainty-driven ensembles of deep architectures for multiclass classification. Application to COVID-19 diagnosis in chest X-ray images
Elsevier Science Publishers B.V.（爱思唯尔科学出版公司）；University of Winchester（温彻斯特大学）；Universidad de Granada（格拉纳达大学）；Universidad de Malaga（马拉加大学）；University of Leicester（莱斯特大学）
DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；RWTH Aachen University（亚琛工业大学）；German National Library of Medicine（德国国家医学图书馆）；University of Cologne（科隆大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
AI4COVID-19: AI Enabled Preliminary Diagnosis for COVID-19 from Cough Samples via an App
University of Oklahoma（俄克拉荷马大学）；AI4Lyf LLC（AI4Lyf有限责任公司）；Kharkiv National Medical University（哈尔科夫国立医科大学）；Michigan State University（密歇根州立大学）；Allergy, Asthma & Immunology Center PC（过敏、哮喘与免疫学中心PC）
Multiscale Attention Guided Network for COVID-19 Diagnosis Using Chest X-ray Images
Hangzhou Dianzi University（杭州电子科技大学）；Communication University of Zhejiang（浙江传媒学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Shanghai Jiao Tong University（上海交通大学）
HRCTCov19 -- A High-Resolution Chest CT Scan Image Dataset for COVID-19 Diagnosis and Differentiation
Isfahan University of Medical Sciences（伊斯法罕医科大学）；Isfahan University of Technology（伊斯法罕理工大学）；Arak University of Medical Sciences（阿拉克医科大学）；Islamic Azad University（伊斯兰阿扎德大学）
DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis
Tencent Jarvis Lab（腾讯Jarvis实验室）；Institute of Computing Technology Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）；University of Science and Technology of China（中国科学技术大学）
冠状病毒继续扰乱我们的日常生活，因为它以指数速度传播。需要迅速发现，以便隔离阳性患者，避免进一步传播。这项工作提出了一种新的卷积神经网络（CNN）架构称为“慢编码CNN。在AI AGAINST COVID-19- Screening X-ray images for COVID-19 Infections competition的测试数据样本上，发现所提出的模型的最佳性能wrt灵敏度，阳性预测值（PPV）为SP=0.67，PP=0.98，SN=0.96和PN=0.52。SP和PP代表COVID-19阳性类别的灵敏度和PPV，而PN和SN代表COVID-19阴性类别的灵敏度和PPV。
COVID-19 Computer-aided Diagnosis through AI-assisted CT Imaging Analysis: Deploying a Medical AI System
University of Ioannina（约阿尼纳大学）；AIandMe SMPC（AIandMe SMPC公司）；National Technical University of Athens（雅典国立技术大学）；Queen Mary University of London（伦敦大学玛丽皇后学院）
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images
Universidade Estadual de Maringá（马里兰州立大学）；Instituto Federal do Paraná（巴拉那联邦学院）；Universidade Tecnológica Federal do Paraná（巴拉那联邦理工大学）；Universidade Federal do Paraná（巴拉那联邦大学）；Università Degli Studi di Padova（帕多瓦大学）；Universidade Federal de Pernambuco（伯南布哥联邦大学）
Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19
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）
A two-step explainable approach for COVID-19 computer-aided diagnosis from chest x-ray images
University of Turin（都灵大学）；Azienda Ospedaliera Città della Salute e della Scienza Presidio Molinette（健康与科学城医院莫利内特分院）；AOU San Luigi Gonzaga（圣路易吉·贡扎加大学医院）
