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10.1007/s10278-025-01752-8 http://scihub22266oqcxt.onion/10.1007/s10278-025-01752-8 41369959!?!41369959 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=41369959&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       J+Imaging+Inform+Med 2025 ; ? (?): ? Nephropedia Template TP {{tp|p=41369959|t=2025. Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance |pdf=|usr=}}{{41369959}} gab.com Text Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance JO: J Imaging Inform Med http://www.kidney.de/pget.php?&tw=1&pmid=41369959 #FOAvip To reduce the amount of required labeled data for lung disease severity classification from chest X-rays (CXRs) under class imbalance, this study applied deep active learning with a Bayesian Neural Network (BNN) approximation and weighted loss function. This retrospective study collected 2319 CXRs from 963 patients (mean age, 59.2 +/- 16.6 years; 481 females) at Emory Healthcare affiliated hospitals between January and November 2020. All patients had clinically confirmed COVID-19. Each CXR was independently labeled by 3 to 6 board-certified radiologists as normal, moderate, or severe. A deep neural network with Monte Carlo Dropout was trained using active learning to classify disease severity. Various acquisition functions were used to iteratively select the most informative samples from an unlabeled pool. Performance was evaluated using accuracy, area under the receiver operating characteristic curve (AU-ROC), and area under the precision-recall curve (AU-PRC). Training time and acquisition time were recorded. Statistical analysis included descriptive metrics and performance comparisons across acquisition strategies. Least Confidence achieved 92.8% accuracy (AU-ROC, 0.95) in binary classification (normal vs. diseased) using 9.24% of the training data. In the multi-class setting, Mean STD achieved 70.5% accuracy (AU-ROC, 0.85) using 21.87% of the labeled data. These methods outperformed more complex and computationally expensive acquisition functions and significantly reduced labeling needs. Deep active learning with BNN approximation and weighted loss effectively reduces labeled data requirements while addressing class imbalance, maintaining or exceeding diagnostic performance. Twit Text FOAVip Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance ????J Imaging Inform Med http://www.kidney.de/pget.php?&tw=1&pmid=41369959 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41369959 #FOAvip English Wikipedia <ref>{{Cite pmid|41369959}}</ref> Deep Active Learning for Lung Disease Severity Classification from Chest X-rays: Learning with Less Data in the Presence of Class Imbalance #MMPMID41369959 Gabriel RM; Zandehshahvar M; van Assen M; Kittisut N; Peters K; De Cecco CN; Adibi A J Imaging Inform Med 2025[Dec]; ? (?): ? PMID41369959show ga To reduce the amount of required labeled data for lung disease severity classification from chest X-rays (CXRs) under class imbalance, this study applied deep active learning with a Bayesian Neural Network (BNN) approximation and weighted loss function. This retrospective study collected 2319 CXRs from 963 patients (mean age, 59.2 +/- 16.6 years; 481 females) at Emory Healthcare affiliated hospitals between January and November 2020. All patients had clinically confirmed COVID-19. Each CXR was independently labeled by 3 to 6 board-certified radiologists as normal, moderate, or severe. A deep neural network with Monte Carlo Dropout was trained using active learning to classify disease severity. Various acquisition functions were used to iteratively select the most informative samples from an unlabeled pool. Performance was evaluated using accuracy, area under the receiver operating characteristic curve (AU-ROC), and area under the precision-recall curve (AU-PRC). Training time and acquisition time were recorded. Statistical analysis included descriptive metrics and performance comparisons across acquisition strategies. Least Confidence achieved 92.8% accuracy (AU-ROC, 0.95) in binary classification (normal vs. diseased) using 9.24% of the training data. In the multi-class setting, Mean STD achieved 70.5% accuracy (AU-ROC, 0.85) using 21.87% of the labeled data. These methods outperformed more complex and computationally expensive acquisition functions and significantly reduced labeling needs. Deep active learning with BNN approximation and weighted loss effectively reduces labeled data requirements while addressing class imbalance, maintaining or exceeding diagnostic performance. ?Deep Active Learning for Lung Disease Severity Classification from Che(epmc).pdf DeepDyve Pubget Overpricing