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10.1109/TMI.2020.3040950 http://scihub22266oqcxt.onion/10.1109/TMI.2020.3040950 33245693!8544953!33245693     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Med+Imaging 2021 ; 40 (3): 879-890 Nephropedia Template TP {{tp|p=33245693|t=2021. Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection |pdf=|usr=}}{{33245693}} gab.com Text Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33245693 #FOAvip Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature. Twit Text FOAVip Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=33245693 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33245693 #FOAvip English Wikipedia <ref>{{Cite pmid|33245693}}</ref> Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection #MMPMID33245693 Zhang J; Xie Y; Pang G; Liao Z; Verjans J; Li W; Sun Z; He J; Li Y; Shen C; Xia Y IEEE Trans Med Imaging 2021[Mar]; 40 (3): 879-890 PMID33245693show ga Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature. |*Deep Learning[MESH] |COVID-19/diagnostic imaging[MESH] |Humans[MESH] |Pneumonia, Viral/*diagnostic imaging[MESH] |Radiographic Image Interpretation, Computer-Assisted/*methods[MESH] |SARS-CoV-2[MESH]Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anoma(epmc).pdf DeepDyve Pubget Overpricing