119 research outputs found

    Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

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    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 provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR

    Content-based image retrieval-- a small sample learning approach.

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    Tao Dacheng.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-75).Abstracts in English and Chinese.Chapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Content-based Image Retrieval --- p.1Chapter 1.2 --- SVM based RF in CBIR --- p.3Chapter 1.3 --- DA based RF in CBIR --- p.4Chapter 1.4 --- Existing CBIR Engines --- p.5Chapter 1.5 --- Practical Applications of CBIR --- p.10Chapter 1.6 --- Organization of this thesis --- p.11Chapter Chapter 2 --- Statistical Learning Theory and Support Vector Machine --- p.12Chapter 2.1 --- The Recognition Problem --- p.12Chapter 2.2 --- Regularization --- p.14Chapter 2.3 --- The VC Dimension --- p.14Chapter 2.4 --- Structure Risk Minimization --- p.15Chapter 2.5 --- Support Vector Machine --- p.15Chapter 2.6 --- Kernel Space --- p.17Chapter Chapter 3 --- Discriminant Analysis --- p.18Chapter 3.1 --- PCA --- p.18Chapter 3.2 --- KPCA --- p.18Chapter 3.3 --- LDA --- p.20Chapter 3.4 --- BDA --- p.20Chapter 3.5 --- KBDA --- p.21Chapter Chapter 4 --- Random Sampling Based SVM --- p.24Chapter 4.1 --- Asymmetric Bagging SVM --- p.25Chapter 4.2 --- Random Subspace Method SVM --- p.26Chapter 4.3 --- Asymmetric Bagging RSM SVM --- p.26Chapter 4.4 --- Aggregation Model --- p.30Chapter 4.5 --- Dissimilarity Measure --- p.31Chapter 4.6 --- Computational Complexity Analysis --- p.31Chapter 4.7 --- QueryGo Image Retrieval System --- p.32Chapter 4.8 --- Toy Experiments --- p.35Chapter 4.9 --- Statistical Experimental Results --- p.36Chapter Chapter 5 --- SSS Problems in KBDA RF --- p.42Chapter 5.1 --- DKBDA --- p.43Chapter 5.1.1 --- DLDA --- p.43Chapter 5.1.2 --- DKBDA --- p.43Chapter 5.2 --- NKBDA --- p.48Chapter 5.2.1 --- NLDA --- p.48Chapter 5.2.2 --- NKBDA --- p.48Chapter 5.3 --- FKBDA --- p.49Chapter 5.3.1 --- FLDA --- p.49Chapter 5.3.2 --- FKBDA --- p.49Chapter 5.4 --- Experimental Results --- p.50Chapter Chapter 6 --- NDA based RF for CBIR --- p.52Chapter 6.1 --- NDA --- p.52Chapter 6.2 --- SSS Problem in NDA --- p.53Chapter 6.2.1 --- Regularization method --- p.53Chapter 6.2.2 --- Null-space method --- p.54Chapter 6.2.3 --- Full-space method --- p.54Chapter 6.3 --- Experimental results --- p.55Chapter 6.3.1 --- K nearest neighbor evaluation for NDA --- p.55Chapter 6.3.2 --- SSS problem --- p.56Chapter 6.3.3 --- Evaluation experiments --- p.57Chapter Chapter 7 --- Medical Image Classification --- p.59Chapter 7.1 --- Introduction --- p.59Chapter 7.2 --- Region-based Co-occurrence Matrix Texture Feature --- p.60Chapter 7.3 --- Multi-level Feature Selection --- p.62Chapter 7.4 --- Experimental Results --- p.63Chapter 7.4.1 --- Data Set --- p.64Chapter 7.4.2 --- Classification Using Traditional Features --- p.65Chapter 7.4.3 --- Classification Using the New Features --- p.66Chapter Chapter 8 --- Conclusion --- p.68Bibliography --- p.7

    A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS

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    The Coronavirus Disease 2019 (COVID-19) has caused massive infections and death toll. Radiological imaging in chest such as computed tomography (CT) has been instrumental in the diagnosis and evaluation of the lung infection which is the common indication in COVID-19 infected patients. The technological advances in artificial intelligence (AI) furthermore increase the performance of imaging tools and support health professionals. CT, Positron Emission Tomography – CT (PET/CT), X-ray, Magnetic Resonance Imaging (MRI), and Lung Ultrasound (LUS) are used for diagnosis, treatment of COVID-19. Applying AI on image acquisition will help automate the process of scanning and providing protection to lab technicians. AI empowered models help radiologists and health experts in making better clinical decisions. We review AI-empowered medical imaging characteristics, image acquisition, computer-aided models that help in the COVID-19 diagnosis, management, and follow-up. Much emphasis is on CT and X-ray with integrated AI, as they are first choice in many hospitals

    Early assessment of lung function in coronavirus patients using invariant markers from chest X-rays images

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    The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation (translation or rotation of the image) as well as scale (i.e., pixel size). The parameters of the MGRF model are learned automatically, given a training set of X-ray images with affected lung regions labeled. An X-ray input to the system undergoes pre-processing to correct for non-uniformity of illumination and to delimit the boundary of the lung, using either a fully-automated segmentation routine or manual delineation provided by the radiologist, prior to the diagnosis. The steps of the proposed methodology are: (i) estimate the Gibbs energy at several different radii to describe the inhomogeneity in lung infection; (ii) compute the cumulative distribution function (CDF) as a new representation to describe the local inhomogeneity in the infected region of lung; and (iii) input the CDFs to a new neural network-based fusion system to determine whether the severity of lung infection is low or high. This approach is tested on 200 clinical X-rays from 200 COVID-19 positive patients, 100 of whom died and 100 who recovered using multiple training/testing processes including leave-one-subject-out (LOSO), tenfold, fourfold, and twofold cross-validation tests. The Gibbs energy for lung pathology was estimated at three concentric rings of increasing radii. The accuracy and Dice similarity coefficient (DSC) of the system steadily improved as the radius increased. The overall CAD system combined the estimated Gibbs energy information from all radii and achieved a sensitivity, specificity, accuracy, and DSC of 100%, 97% ± 3%, 98% ± 2%, and 98% ± 2%, respectively, by twofold cross validation. Alternative classification algorithms, including support vector machine, random forest, naive Bayes classifier, K-nearest neighbors, and decision trees all produced inferior results compared to the proposed neural network used in this CAD system. The experiments demonstrate the feasibility of the proposed system as a novel tool to objectively assess disease severity and predict mortality in COVID-19 patients. The proposed tool can assist physicians to determine which patients might require more intensive clinical care, such a mechanical respiratory support
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