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10.1007/978-3-030-52791-4_27 http://scihub22266oqcxt.onion/10.1007/978-3-030-52791-4_27 C7340943!7340943!C7340943     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Medical+Image+Understanding+and+Analysis 2020 ; 1248 (ä): 346-58 Nephropedia Template TP {{tp|p=C7340943|t=2020. Discovering Unknown Diseases with Explainable Automated Medical Imaging |pdf=|usr=}}{{C7340943}} gab.com Text Discovering Unknown Diseases with Explainable Automated Medical Imaging JO: Medical Image Understanding and Analysis http://www.kidney.de/pget.php?&tw=1&pmid=C7340943 #FOAvip Deep neural network (DNN) classifiers have attained remarkable performance in diagnosing known diseases when the models are trained on a large amount of data from known diseases. However, DNN classifiers trained on known diseases usually fail when they confront new diseases such as COVID-19. In this paper, we propose a new deep learning framework and pipeline for explainable medical imaging that can classify known diseases as well as detect new/unknown diseases when the models are only trained on known disease images. We first provide in-depth mathematical analysis to explain the overconfidence phenomena and present the calibrated confidence that can mitigate the overconfidence. Using calibrated confidence, we design a decision engine to determine if a medical image belongs to some known diseases or a new disease. At last, we introduce a new visual explanation to further reveal the suspected region inside each image. Using both Skin Lesion and Chest X-Ray datasets, we validate that our framework significantly improves the accuracy of new disease discovery, i.e., distinguish COVID-19 from pneumonia without seeing any COVID-19 data during training. We also qualitatively show that our visual explanations are highly consistent with doctors? ground truth. While our work was not designed to target COVID-19, our experimental validation using the real world COVID-19 cases/data demonstrates the general applicability of our pipeline for different diseases based on medical imaging. Twit Text FOAVip Discovering Unknown Diseases with Explainable Automated Medical Imaging ????Medical Image Understanding and Analysis http://www.kidney.de/pget.php?&tw=1&pmid=C7340943 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=C7340943 #FOAvip English Wikipedia <ref>{{Cite pmid|C7340943}}</ref> Discovering Unknown Diseases with Explainable Automated Medical Imaging #MMPMIDC7340943 Tang C Medical Image Understanding and Analysis 2020[]; 1248 (ä): 346-58 PMIDC7340943show ga Deep neural network (DNN) classifiers have attained remarkable performance in diagnosing known diseases when the models are trained on a large amount of data from known diseases. However, DNN classifiers trained on known diseases usually fail when they confront new diseases such as COVID-19. In this paper, we propose a new deep learning framework and pipeline for explainable medical imaging that can classify known diseases as well as detect new/unknown diseases when the models are only trained on known disease images. We first provide in-depth mathematical analysis to explain the overconfidence phenomena and present the calibrated confidence that can mitigate the overconfidence. Using calibrated confidence, we design a decision engine to determine if a medical image belongs to some known diseases or a new disease. At last, we introduce a new visual explanation to further reveal the suspected region inside each image. Using both Skin Lesion and Chest X-Ray datasets, we validate that our framework significantly improves the accuracy of new disease discovery, i.e., distinguish COVID-19 from pneumonia without seeing any COVID-19 data during training. We also qualitatively show that our visual explanations are highly consistent with doctors? ground truth. While our work was not designed to target COVID-19, our experimental validation using the real world COVID-19 cases/data demonstrates the general applicability of our pipeline for different diseases based on medical imaging. äDiscovering Unknown Diseases with Explainable Automated Medical Imagin(epmc).pdf DeepDyve Pubget Overpricing