A machine learning analysis of the relationship between some underlying medical conditions and COVID-19 susceptibility

Analysis of the Production Strategy of Mask Types in the COVID-19 Environment

Since the outbreak of the COVID-19 in December 2019, medical protective equipment such as disposable medical masks and KN95 masks have become essential resources for the public. Enterprises in all sectors of society have also transformed the production of medical masks. After the outbreak, how to choose the right time to produce medical protective masks, and what type of medical masks to produce will play a positive role in preventing and controlling the epidemic in a short time. In this regard, the evolutionary game competition analysis will be conducted through the relevant data of disposabl…

Deep Learning Prediction of Severe Health Risks for Pediatric COVID-19 Patients with a Large Feature Set in 2021 BARDA Data Challenge

Most children infected with COVID-19 have no or mild symptoms and can recover automatically by themselves, but some pediatric COVID-19 patients need to be hospitalized or even to receive intensive medical care (e.g., invasive mechanical ventilation or cardiovascular support) to recover from the illnesses. Therefore, it is critical to predict the severe health risk that COVID-19 infection poses to children to provide precise and timely medical care for vulnerable pediatric COVID-19 patients. However, predicting the severe health risk for COVID-19 patients including children remains a significan…

AI-MIA: COVID-19 Detection & Severity Analysis through Medical Imaging

This paper presents the baseline approach for the organized 2nd Covid-19 Competition, occurring in the framework of the AIMIA Workshop in the European Conference on Computer Vision (ECCV 2022). It presents the COV19-CT-DB database which is annotated for COVID-19 detction, consisting of about 7,700 3-D CT scans. Part of the database consisting of Covid-19 cases is further annotated in terms of four Covid-19 severity conditions. We have split the database and the latter part of it in training, validation and test datasets. The former two datasets are used for training and validation of machine l…

A feasibility study proposal of the predictive model to enable the prediction of population susceptibility to COVID-19 by analysis of vaccine utilization for advising deployment of a booster dose

With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or Omicron spreading around the globe, there is concern that the COVID-19 pandemic will not end soon and that it will be a race against time until a more contagious and virulent variant emerges. One of the most promising approaches for preventing virus propagation is to maintain continuous high vaccination efficacy among the population, thereby strengthening the population protective effect and preventing the majority of infection in the vaccinated population, as is known to occur with the Omicron variant frequently. Cou…

Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale

Pandemic outbreaks such as COVID-19 occur unexpectedly, and need immediate action due to their potential devastating consequences on global health. Point-of-care routine assessments such as electrocardiogram (ECG), can be used to develop prediction models for identifying individuals at risk. However, there is often too little clinically-annotated medical data, especially in early phases of a pandemic, to develop accurate prediction models. In such situations, historical pre-pandemic health records can be utilized to estimate a preliminary model, which can then be fine-tuned based on limited av…

A Two-Stage Federated Transfer Learning Framework in Medical Images Classification on Limited Data: A COVID-19 Case Study

COVID-19 pandemic has spread rapidly and caused a shortage of global medical resources. The efficiency of COVID-19 diagnosis has become highly significant. As deep learning and convolutional neural network (CNN) has been widely utilized and been verified in analyzing medical images, it has become a powerful tool for computer-assisted diagnosis. However, there are two most significant challenges in medical image classification with the help of deep learning and neural networks, one of them is the difficulty of acquiring enough samples, which may lead to model overfitting. Privacy concerns mainl…

MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction

The COVID-19 pandemic has caused devastating economic and social disruption, straining the resources of healthcare institutions worldwide. This has led to a nationwide call for models to predict hospitalization and severe illness in patients with COVID-19 to inform distribution of limited healthcare resources. We respond to one of these calls specific to the pediatric population. To address this challenge, we study two prediction tasks for the pediatric population using electronic health records: 1) predicting which children are more likely to be hospitalized, and 2) among hospitalized childre…

Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies

The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical …

DeltaNet:Conditional Medical Report Generation for COVID-19 Diagnosis

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radiologists, we propose DeltaNet to generate medical reports automatically. Different from typical image captioning approaches that generate reports with an encoder and a decoder, DeltaNet applies a conditional generation process. In particular…

Feasibility Study of Multi-Site Split Learning for Privacy-Preserving Medical Systems under Data Imbalance Constraints in COVID-19, X-Ray, and Cholesterol Dataset

It seems as though progressively more people are in the race to upload content, data, and information online; and hospitals haven't neglected this trend either. Hospitals are now at the forefront for multi-site medical data sharing to provide groundbreaking advancements in the way health records are shared and patients are diagnosed. Sharing of medical data is essential in modern medical research. Yet, as with all data sharing technology, the challenge is to balance improved treatment with protecting patient's personal information. This paper provides a novel split learning algorithm coined th…

CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis

Due to the characteristics of COVID-19, the epidemic develops rapidly and overwhelms health service systems worldwide. Many patients suffer from systemic life-threatening problems and need to be carefully monitored in ICUs. Thus the intelligent prognosis is in an urgent need to assist physicians to take an early intervention, prevent the adverse outcome, and optimize the medical resource allocation. However, in the early stage of the epidemic outbreak, the data available for analysis is limited due to the lack of effective diagnostic mechanisms, rarity of the cases, and privacy concerns. In th…

DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19

Early detection of the coronavirus disease 2019 (COVID-19) helps to treat patients timely and increase the cure rate, thus further suppressing the spread of the disease. In this study, we propose a novel deep learning based detection and similar case recommendation network to help control the epidemic. Our proposed network contains two stages: the first one is a lung region segmentation step and is used to exclude irrelevant factors, and the second is a detection and recommendation stage. Under this framework, in the second stage, we develop a dual-children network (DuCN) based on a pre-traine…

Predicting COVID-19 Patient Shielding: A Comprehensive Study

There are many ways machine learning and big data analytics are used in the fight against the COVID-19 pandemic, including predictions, risk management, diagnostics, and prevention. This study focuses on predicting COVID-19 patient shielding -- identifying and protecting patients who are clinically extremely vulnerable from coronavirus. This study focuses on techniques used for the multi-label classification of medical text. Using the information published by the United Kingdom NHS and the World Health Organisation, we present a novel approach to predicting COVID-19 patient shielding as a mult…

An early prediction of covid-19 associated hospitalization surge using deep learning approach

The global pandemic caused by COVID-19 affects our lives in all aspects. As of September 11, more than 28 million people have tested positive for COVID-19 infection, and more than 911,000 people have lost their lives in this virus battle. Some patients can not receive appropriate medical treatment due the limits of hospitalization volume and shortage of ICU beds. An estimated future hospitalization is critical so that medical resources can be allocated as needed. In this study, we propose to use 4 recurrent neural networks to infer hospitalization change for the following week compared with th…

Blockchain based Privacy-Preserved Federated Learning for Medical Images: A Case Study of COVID-19 CT Scans

Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and training the model in a single organization, which is most common weakness due to the privacy and security of raw data communication. To solve this challenging task, we propose a blockchain-based federated learning framework that provides collaborative data training solutions by coordinating multiple hospitals to train and share encrypted federated models withou…

Effective Transfer Learning for Identifying Similar Questions: Matching User Questions to COVID-19 FAQs

People increasingly search online for answers to their medical questions but the rate at which medical questions are asked online significantly exceeds the capacity of qualified people to answer them. This leaves many questions unanswered or inadequately answered. Many of these questions are not unique, and reliable identification of similar questions would enable more efficient and effective question answering schema. COVID-19 has only exacerbated this problem. Almost every government agency and healthcare organization has tried to meet the informational need of users by building online FAQs,…

Dynamic Fusion based Federated Learning for COVID-19 Detection

Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy. This causes the issue of insufficient datasets for training the image classification model. Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received updates of local models trained by clients without exchanging clients' local data. Nevertheless…

Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks

In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this expe…

Cov3d: Detection of the presence and severity of COVID-19 from CT scans using 3D ResNets

Deep learning has been used to assist in the analysis of medical imaging. One such use is the classification of Computed Tomography (CT) scans when detecting for COVID-19 in subjects. This paper presents Cov3d, a three dimensional convolutional neural network for detecting the presence and severity of COVID19 from chest CT scans. Trained on the COV19-CT-DB dataset with human expert annotations, it achieves a macro f1 score of 0.9476 on the validation set for the task of detecting the presence of COVID19. For the task of classifying the severity of COVID19, it achieves a macro f1 score of 0.755…

BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients

This paper describes BIMCV COVID-19+, a large dataset from the Valencian Region Medical ImageBank (BIMCV) containing chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests. The findings have been mapped onto standard Unified Medical Language System (UMLS) terminology and cover a wide spectrum of thoracic entities, unlike the considerab…

Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review

This review presents a comprehensive study on the role of GANs in addressing the challenges related to COVID-19 data scarcity and diagnosis. It is the first review that summarizes the different GANs methods and the lungs images datasets for COVID-19. It attempts to answer the questions related to applications of GANs, popular GAN architectures, frequently used image modalities, and the availability of source code. This review included 57 full-text studies that reported the use of GANs for different applications in COVID-19 lungs images data. Most of the studies (n=42) used GANs for data augmen…

COVID-19 CT Image Synthesis with a Conditional Generative Adversarial Network

Coronavirus disease 2019 (COVID-19) is an ongoing global pandemic that has spread rapidly since December 2019. Real-time reverse transcription polymerase chain reaction (rRT-PCR) and chest computed tomography (CT) imaging both play an important role in COVID-19 diagnosis. Chest CT imaging offers the benefits of quick reporting, a low cost, and high sensitivity for the detection of pulmonary infection. Recently, deep-learning-based computer vision methods have demonstrated great promise for use in medical imaging applications, including X-rays, magnetic resonance imaging, and CT imaging. Howeve…

FLOP: Federated Learning on Medical Datasets using Partial Networks

The outbreak of COVID-19 Disease due to the novel coronavirus has caused a shortage of medical resources. To aid and accelerate the diagnosis process, automatic diagnosis of COVID-19 via deep learning models has recently been explored by researchers across the world. While different data-driven deep learning models have been developed to mitigate the diagnosis of COVID-19, the data itself is still scarce due to patient privacy concerns. Federated Learning (FL) is a natural solution because it allows different organizations to cooperatively learn an effective deep learning model without sharing…

Homomorphic Encryption and Federated Learning based Privacy-Preserving CNN Training: COVID-19 Detection Use-Case

Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learning techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applica…

使用ProGAN和SMA优化的ResNet进行不平衡数据的医学图像分类：应用于新冠肺炎

Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19

本文提出基于ProGAN和SMA优化的ResNet模型，用于解决医学图像分类中的不平衡数据问题，通过生成合成数据和优化超参数，提升新冠肺炎检测的准确率。

Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognit…

Vision Transformer based COVID-19 Detection using Chest X-rays

COVID-19 is a global pandemic, and detecting them is a momentous task for medical professionals today due to its rapid mutations. Current methods of examining chest X-rays and CT scan requires profound knowledge and are time consuming, which suggests that it shrinks the precious time of medical practitioners when people's lives are at stake. This study tries to assist this process by achieving state-of-the-art performance in classifying chest X-rays by fine-tuning Vision Transformer(ViT). The proposed approach uses pretrained models, fine-tuned for detecting the presence of COVID-19 disease on…

MIA-COV19D: COVID-19 Detection through 3-D Chest CT Image Analysis

Early and reliable COVID-19 diagnosis based on chest 3-D CT scans can assist medical specialists in vital circumstances. Deep learning methodologies constitute a main approach for chest CT scan analysis and disease prediction. However, large annotated databases are necessary for developing deep learning models that are able to provide COVID-19 diagnosis across various medical environments in different countries. Due to privacy issues, publicly available COVID-19 CT datasets are highly difficult to obtain, which hinders the research and development of AI-enabled diagnosis methods of COVID-19 ba…

Spatio-Temporal Split Learning for Privacy-Preserving Medical Platforms: Case Studies with COVID-19 CT, X-Ray, and Cholesterol Data

Machine learning requires a large volume of sample data, especially when it is used in high-accuracy medical applications. However, patient records are one of the most sensitive private information that is not usually shared among institutes. This paper presents spatio-temporal split learning, a distributed deep neural network framework, which is a turning point in allowing collaboration among privacy-sensitive organizations. Our spatio-temporal split learning presents how distributed machine learning can be efficiently conducted with minimal privacy concerns. The proposed split learning consi…

COVID-19 Disease Identification on Chest-CT images using CNN and VGG16

A newly identified coronavirus disease called COVID-19 mainly affects the human respiratory system. COVID-19 is an infectious disease caused by a virus originating in Wuhan, China, in December 2019. Early diagnosis is the primary challenge of health care providers. In the earlier stage, medical organizations were dazzled because there were no proper health aids or medicine to detect a COVID-19. A new diagnostic tool RT-PCR (Reverse Transcription Polymerase Chain Reaction), was introduced. It collects swab specimens from the patient's nose or throat, where the COVID-19 virus gathers. This metho…

Enhanced detection of the presence and severity of COVID-19 from CT scans using lung segmentation

Improving automated analysis of medical imaging will provide clinicians more options in providing care for patients. The 2023 AI-enabled Medical Image Analysis Workshop and Covid-19 Diagnosis Competition (AI-MIA-COV19D) provides an opportunity to test and refine machine learning methods for detecting the presence and severity of COVID-19 in patients from CT scans. This paper presents version 2 of Cov3d, a deep learning model submitted in the 2022 competition. The model has been improved through a preprocessing step which segments the lungs in the CT scan and crops the input to this region. It …

医学诊断中深度学习的多模态奇迹：COVID-19检测综合综述

Multimodal Marvels of Deep Learning in Medical Diagnosis: A Comprehensive Review of COVID-19 Detection

本研究以COVID-19为例，系统综述了多模态深度学习在医学诊断中的应用，比较了11种模型，发现MobileNet在图像和语音数据上准确率最高，BiGRU在文本分类上最优。

Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications

The novel Coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew in 2020. It becomes the most unprecedent challenge to analytic epidemiology in general and signal processing theories in specific. Given its high contingency nature and adverse effects across the world, it is important to develop efficient processing/learning models to overcome this pandemic and be prepared for potential future ones. In this regard, medical imaging plays an important role for the management of COVID-19. Human-centered interpretation of medical images is, however, tedious and can be s…

Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic. This research leverages a database of 92 patients with confirmed SARS-CoV-2 laboratory tests between 18th Jan. 2020 and 5th Mar. 2020, in Zhuhai, China, to identify …

Using natural language processing and structured medical data to phenotype patients hospitalized due to COVID-19

To identify patients who are hospitalized because of COVID-19 as opposed to those who were admitted for other indications, we compared the performance of different computable phenotype definitions for COVID-19 hospitalizations that use different types of data from the electronic health records (EHR), including structured EHR data elements, provider notes, or a combination of both data types. And conduct a retrospective data analysis utilizing chart review-based validation. Participants are 586 hospitalized individuals who tested positive for SARS-CoV-2 during January 2022. We used natural lang…

Explainable Multi-class Classification of the CAMH COVID-19 Mental Health Data

Application of Machine Learning algorithms to the medical domain is an emerging trend that helps to advance medical knowledge. At the same time, there is a significant a lack of explainable studies that promote informed, transparent, and interpretable use of Machine Learning algorithms. In this paper, we present explainable multi-class classification of the Covid-19 mental health data. In Machine Learning study, we aim to find the potential factors to influence a personal mental health during the Covid-19 pandemic. We found that Random Forest (RF) and Gradient Boosting (GB) have scored the hig…

用于脑肿瘤 MRI 与 COVID-19 胸部 X 光图像的可视化可解释深度学习模型

Visual Interpretable and Explainable Deep Learning Models for Brain Tumor MRI and COVID-19 Chest X-ray Images

本文通过基于自适应路径的梯度积分归因方法，对脑肿瘤 MRI 和 COVID-19 胸部 X 光图像的深度学习预测进行可视化解释，揭示模型推理依据与偏差，从而提升医疗领域对深度学习的信任。

集成与测试增强用于基于3D CT扫描的新冠肺炎检测及新冠肺炎领域自适应

Ensembling and Test Augmentation for Covid-19 Detection and Covid-19 Domain Adaptation from 3D CT-Scans

本文针对COV19D竞赛，提出基于PDAtt-Unet分割和多种3D CNN骨干网络集成及测试增强的方法，用于新冠肺炎检测与领域自适应，相比基线F1分数提升14%。

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review

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,…

MEDNC：用于COVID-19诊断的多集成深度神经网络

MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis

本文提出受迁移学习启发的多集成深度神经网络MEDNC，利用CT图像自动诊断COVID-19，并在多个医学图像数据集上验证其高准确率与广泛适用性。

Evaluation of Contemporary Convolutional Neural Network Architectures for Detecting COVID-19 from Chest Radiographs

Interpreting chest radiograph, a.ka. chest x-ray, images is a necessary and crucial diagnostic tool used by medical professionals to detect and identify many diseases that may plague a patient. Although the images themselves contain a wealth of valuable information, their usefulness may be limited by how well they are interpreted, especially when the reviewing radiologist may be fatigued or when or an experienced radiologist is unavailable. Research in the use of deep learning models to analyze chest radiographs yielded impressive results where, in some instances, the models outperformed pract…

A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging

Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT-PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess, and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images. A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality resear…

The Prominence of Artificial Intelligence in COVID-19

In December 2019, a novel virus called COVID-19 had caused an enormous number of causalities to date. The battle with the novel Coronavirus is baffling and horrifying after the Spanish Flu 2019. While the front-line doctors and medical researchers have made significant progress in controlling the spread of the highly contiguous virus, technology has also proved its significance in the battle. Moreover, Artificial Intelligence has been adopted in many medical applications to diagnose many diseases, even baffling experienced doctors. Therefore, this survey paper explores the methodologies propos…

Fuzzy Expert Systems for Prediction of ICU Admission in Patients with COVID-19

The pandemic COVID-19 disease has had a dramatic impact on almost all countries around the world so that many hospitals have been overwhelmed with Covid-19 cases. As medical resources are limited, deciding on the proper allocation of these resources is a very crucial issue. Besides, uncertainty is a major factor that can affect decisions, especially in medical fields. To cope with this issue, we use fuzzy logic (FL) as one of the most suitable methods in modeling systems with high uncertainty and complexity. We intend to make use of the advantages of FL in decisions on cases that need to treat…

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19)

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities l…

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)

With the rapid development of COVID-19 into a global pandemic, there is an ever more urgent need for cheap, fast and reliable tools that can assist physicians in diagnosing COVID-19. Medical imaging such as CT can take a key role in complementing conventional diagnostic tools from molecular biology, and, using deep learning techniques, several automatic systems were demonstrated promising performances using CT or X-ray data. Here, we advocate a more prominent role of point-of-care ultrasound imaging to guide COVID-19 detection. Ultrasound is non-invasive and ubiquitous in medical facilities ar…

Self-supervision for health insurance claims data: a Covid-19 use case

In this work, we modify and apply self-supervision techniques to the domain of medical health insurance claims. We model patients' healthcare claims history analogous to free-text narratives, and introduce pre-trained `prior knowledge', later utilized for patient outcome predictions on a challenging task: predicting Covid-19 hospitalization, given a patient's pre-Covid-19 insurance claims history. Results suggest that pre-training on insurance claims not only produces better prediction performance, but, more importantly, improves the model's `clinical trustworthiness' and model stability/relia…

Estimating Uncertainty and Interpretability in Deep Learning for Coronavirus (COVID-19) Detection

Deep Learning has achieved state of the art performance in medical imaging. However, these methods for disease detection focus exclusively on improving the accuracy of classification or predictions without quantifying uncertainty in a decision. Knowing how much confidence there is in a computer-based medical diagnosis is essential for gaining clinicians trust in the technology and therefore improve treatment. Today, the 2019 Coronavirus (SARS-CoV-2) infections are a major healthcare challenge around the world. Detecting COVID-19 in X-ray images is crucial for diagnosis, assessment and treatmen…

Foresight-England：面向COVID-19大流行期间医疗事件预测的全国规模电子健康记录生成式AI模型的开发

Foresight-England: Development of a National-Scale Generative AI Model of Electronic Health Records for Medical Event Prediction across the COVID-19 Pandemic

本研究开发了首个全国规模的电子健康记录生成式基础模型Foresight-E，在6100万患者数据上训练，可零样本预测医疗事件，为大流行期间的医疗事件预测提供了方法模板。

Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications

Effective representation learning is the key in improving model performance for medical image analysis. In training deep learning models, a compromise often must be made between performance and trust, both of which are essential for medical applications. Moreover, models optimized with cross-entropy loss tend to suffer from unwarranted overconfidence in the majority class and over-cautiousness in the minority class. In this work, we integrate a new surrogate loss with self-supervised learning for computer-aided screening of COVID-19 patients using radiography images. In addition, we adopt a ne…

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning enables us to bootstrap the training by exploiting unlabeled data, the generic self-supervised methods for natural images do not sufficiently incorporate the context. For medical images, a desirable method should be sensitive enough to detect deviation from normal-appearing tissue of each anatomical region; here, anatomy is the context. We introduce a novel app…

Pre-text Representation Transfer for Deep Learning with Limited Imbalanced Data : Application to CT-based COVID-19 Detection

Annotating medical images for disease detection is often tedious and expensive. Moreover, the available training samples for a given task are generally scarce and imbalanced. These conditions are not conducive for learning effective deep neural models. Hence, it is common to 'transfer' neural networks trained on natural images to the medical image domain. However, this paradigm lacks in performance due to the large domain gap between the natural and medical image data. To address that, we propose a novel concept of Pre-text Representation Transfer (PRT). In contrast to the conventional transfe…

COVID-19 CT扫描与未整理报告的零样本多标签分类的实证分析

An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports

本研究利用医院非结构化数据和CT扫描的细粒度细节，基于对比视觉语言学习进行零样本多标签分类，辅助放射科医生检测肺栓塞及肺部复杂病变，为医学多模态预训练中细粒度任务提供实证分析。

Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detection

As the COVID-19 pandemic puts pressure on healthcare systems worldwide, the computed tomography image based AI diagnostic system has become a sustainable solution for early diagnosis. However, the model-wise vulnerability under adversarial perturbation hinders its deployment in practical situation. The existing adversarial training strategies are difficult to generalized into medical imaging field challenged by complex medical texture features. To overcome this challenge, we propose a Contour Attention Preserving (CAP) method based on lung cavity edge extraction. The contour prior features are…