Parkinson’s disease (PD) is a degenerative and progressive neurological condition . Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTscan . The proposed system may effectively aid medical workers in the early diagnosis of Parkinson’s Disease . It could be concluded that the proposed system, in […]
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert . We propose a novel embedding generation technique that leverages SciBERT language model pretrained on a large multi-domain corpus of scientific publications and fine-tuned for domain adaptation on the CORD-19 dataset . The conducted manual evaluation by […]
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 . 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 […]
Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019 . As a result, accurate and reliable advance warning system for the early diagnosis has now become a priority . Both compact classifiers and deep learning approaches are considered in this study . A detailed set […]
The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world . Detecting this disease from radiography and radiology images is perhaps one of the fastest way to diagnose the patients . We evaluated these models on the remaining 3,000 images, and most of these networks achieved a sensitivity rate […]
The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia . The current diagnostic procedure of COVID-20 follows reverse-transcriptase polymerase polymerases polymerase chain reaction (RT-PCR) based approach . This article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches . […]
The increased availability of X-ray image archives has triggered a growing interest in deep learning techniques . We investigate a powerful network architecture in detail: the ResNet-50… Building on prior work in this domain, we consider transfer learning with and without fine-tuning as well as the training of a dedicated X-rays network from scratch . […]
Pneumonia is an infection that inflames the lungs’ air sacs of a human . Using a combination of GAN and deep transfer models proved it is efficiency according to testing accuracy measurement . AlexNet, GoogLeNet, Squeeznet, and Resnet18 are selected as deep transfer learning models to detect the pneumonia from chest x-rays . The research […]
COVID-19 (also known as 2019 Novel Coronavirus) first emerged in Wuhan, China and spread across the globe with unprecedented effect and has now become the greatest crisis of the modern era . The lack of a publicly available dataset of X-ray and CT images makes the design of such AI tools a challenging task . […]
Chest X-rays are among the most commonly used medical image modalities . The proposed solution has the potential to cut in half the number of disease-free CXRs examined by radiologists . The probability threshold for classification is optimized for 100% precision for the normal class, ensuring no sick patients are released . At this threshold […]
Standard breast cancer screening involves the acquisition of two mammography X-ray projections for each breast . We introduce a deep learning, patch-based Siamese network for lesion matching in dual-view mammography . Our locally-fitted approach generates a joint patch pair representation and comparison with a shared configuration between the two views . We performed a comprehensive […]
The 2019 novel coronavirus (COVID-19) has spread rapidly all over the world and it is affecting the whole society . The current gold standard test for screening patients is the polymerase chain reaction test… However, the COV-19 test kits are not widely available and time-consuming . As an alternative, chest X-rays are being considered for […]
A GAN with deep transfer learning for coronavirus detection in chest x-ray images is presented . The lack of benchmark datasets for COVID-19 is the main motivation of this research . The GAN helps in generating more images from the original dataset to be 30 times larger than the originally collected dataset . The number […]
Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients . The reduction of CT radiation dose compromises the signal-to-noise ratio, and may compromise the image quality and the diagnostic performance . A novel feature of our approach is that an initial […]
Study aimed to develop and validate computer-aided diagnosis (CXDx) system for classification between pneumonia, non-COVID-19 pneumonia, and the healthy on chest X-ray images . The proposed CADx system utilized VGG16 as a pre-trained model and combination of conventional method and mixup as data augmentation methods . The three-category accuracy of the CAD system was 83.6% […]
Deep neural networks often suffer from degraded performance when applied to new test datasets with domain shift . In this way, the segmentation DNN learned from the source domain is able to be directly generalized to the transformed test image . Our domain adaptation procedure is unsupervised, without using any target domain labels . We […]
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Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images… The major challenge lies in the inadequate public public COVI-19 datasets . The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general […]
The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel . X-ray imaging is an easily accessible tool that can be an excellent alternative in the diagnosis . A public database was created by the authors […]
COVID-CXNet uses deep convolutional neural networks in a large dataset . It is demonstrated that simple models, alongside the majority of pretrained networks in the literature, focus on irrelevant features for decision-making . This powerful model is capable of detecting the novel coronavirus pneumonia based on relevant and meaningful features with precise localization . It’s […]
The state-of-the-art performances we achieved show that, with the help of artificial intelligence, chest X-Rays can become a cheap and accurate auxiliary method for COVID-19 diagnosis . The heatmaps generated by LRP improve the interpretability of the deep neural networks and indicate an analytical path for future research on diagnosis . We were able to […]
COVID-19 has resulted in an ongoing pandemic and as of 12 June 2020, has caused more than 7.4 million cases and over 418,000 deaths . Online communities, forums, and social media provide potential venues to search for relevant questions and answers, or post questions and seek answers from other members . Such models are rarely […]
Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID- 19 based on […]
Online search query frequency time series to gain insights about the prevalence of COVID-19 in multiple countries. We first develop unsupervised modelling techniques based on identified symptom categories by United Kingdom’s National Health Service. We then propose ways for minimising an expected bias in these signals partially generated by the early and continuous exposure to […]
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep-learning to COVID classification of CXR images. We […]
The pandemic of COVID-19 has caused millions of infectious. Due to the false-negative rate and the time cost of conventional RT-PCR tests, X-ray images and Computed Tomography (CT) images based diagnosing become widely adopted. Researchers of the computer vision area have developed many automatic diagnosing models to help radiologists and pro-mote the diagnosing accuracy. 62 […]
Lung cancer screening plays an important role in preventive care. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the transfer learning scheme was […]
Deep learning has thrived by training on large-scale datasets. In many applications, such as medical image diagnosis, getting massive amount of data is still prohibitive due to privacy, lack of acquisition homogeneity and annotation cost. In this scenario, transfer learning from natural image collections is a standard practice that attempts to tackle shape, texture and […]
COVID-MobileXpert is a lightweight deep neural network (DNN) based mobile app that can use noisy snapshots of chest X-ray (CXR) for point-of-care COVID-19 screening. It uses a pre-trained attending physician (AP) network that extracts CXR imaging features from large scale of lung disease CXr images. A fine-tuned resident fellow (RF) network learns the essential CX […]
The spread of COVID-19 has become a significant and troubling aspect of society in 2020. Many disease detection models do not incorporate the wealth of social media data that can be utilized for modeling and predicting its spread. To answer this, we propose the task of cross-lingual transfer learning for epidemiological alignment. Utilizing both macro […]
Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID 19-related consultations. We collected […]
Accurate segmentation of lung and infection in COVID-19 CT scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution. To promote the development of annotation-efficient deep learning methods, we built three benchmarks […]
Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon in the right time and thus an early diagnosis of pneumonia is vital. The aim of this paper is to automatically detect bacterial and viral pneumonia using digital x-ray images. […]
TX-Ray expresses neurons as feature preference distributions to quantify fine-grained knowledge transfer or adaptation and guide human analysis. It can identify prunable neurons for model compression with improved test set generalization and it can reveal how early stages of self-supervision automatically learn linguistic abstractions like parts-of-speech. We find that TX- Ray can identify Prunable Neurons […]
