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Low‐light image/video enhancement is a challenging task when images or video are captured under harsh lighting conditions. Existing methods mostly formulate this task as an image‐to‐image conversion task via supervised or unsupervised learning. However, such conversion methods require an extremely large amount of data for training, whether paired or unpaired. In addition, these methods are restricted to specific training data, making it difficult for the trained model to enhance other types of images or video. In this paper, we explore a novel, fast and flexible, zero‐shot, low‐light image or video enhancement framework. Without relying on prior training or relationships among neighboring frames, we are committed to estimating the illumination of the input image/frame by a well‐designed network. The proposed zero‐shot, low‐light image/video enhancement architecture includes illumination estimation and residual correction modules. The network architecture is very concise and does not require any paired or unpaired data during training, which allows low‐light enhancement to be performed with several simple iterations. Despite its simplicity, we show that the method is fast and generalizes well to diverse lighting conditions. Many experiments on various images and videos qualitatively and quantitatively demonstrate the advantages of our method over state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Background: The interpretation of coronary computed tomography angiography (CCTA) stenosis may be difficult among radiologists of different experience levels. Artificial intelligence (AI) may improve the diagnostic performance. Purpose: To investigate whether the diagnostic performance and time efficiency of radiologists with different levels of experience in interpreting CCTA images could be improved by using CCTA with AI assistance (CCTA-AI). Material and Methods: This analysis included 200 patients with complete CCTA and invasive coronary angiography (ICA) data, using ICA results as the reference. Eighteen radiologists were divided into three levels based on experience (Levels I, II, and III), and the three levels were divided into groups without (Groups 1, 2, and 3) and with (Groups 4, 5, and 6) AI assistance, totaling six groups (to avoid reader recall bias). The average sensitivity, specificity, NPV, PPV, and AUC were reported for the six groups and CCTA-AI at the patient, vessel, and segment levels. The interpretation time in the groups with and without CCTA-AI was recorded. Results: Compared to the corresponding group without CCTA-AI, the Level I group with CCTA-AI had improved sensitivity (75.0% vs. 83.0% on patient-based; P = 0.003). At Level III, the specificity was better with CCTA-AI. The median interpretation times for the groups with and without CCTA-AI were 413 and 615 s, respectively (P < 0.001). Conclusion: CCTA-AI could assist with and improve the diagnostic performance of radiologists with different experience levels, with Level I radiologists exhibiting improved sensitivity and Level III radiologists exhibiting improved specificity. The use of CCTA-AI could shorten the training time for radiologists. [ABSTRACT FROM AUTHOR]

Objectives: To develop a computer‐aided diagnosis (CAD) system for distinguishing malignant from benign pulmonary nodules on computed tomography (CT) scans, and to assess whether the diagnostic performance of radiologists with different experiences can be improved with the assistant of CAD. Materials and methods: A total of 857 malignant nodules from 601 patients and 426 benign nodules from 278 patients were retrospectively collected from four hospitals. In this study, we exploited convolutional neural network in the framework of deep learning to classify whether a nodule was benign or malignant. A total of 745 malignant nodules and 370 benign nodules were used as the training data of our CAD system. The remaining 112 malignant nodules and 56 benign nodules were used as the test data. The participants were two senior chest radiologists, two secondary chest radiologists, and two junior radiology residents. The readers estimated the likelihood of malignancy of pulmonary nodules first without and then with CAD output. Receiver‐operating characteristic (ROC) curve was used to evaluate readers' diagnostic performance. Results: When a threshold level of 58% was used to estimate the likelihood of malignancy, the sensitivity, specificity, and diagnostic accuracy values of our CAD scheme alone were 93.8%, 83.9%, and 90.5%, respectively. For all six readers, the mean area under the ROC curve (Az) values without and with CAD system were 0.913 and 0.938, respectively. For each reader, there is a large difference in Az values that assessed without and with CAD system. With CAD output, the readers made correct changes an average of 15.7 times and incorrect changes an average of 2 times. Conclusion: Our CAD system significantly improved the diagnostic performance of readers regardless of their experience levels for assessment of the likelihood of malignancy of pulmonary nodules. [ABSTRACT FROM AUTHOR]

In this paper, we prove that the Cauchy problem for a non-linear wave equation (1)has a unique global generalized solutions in and a unique global classical solution in . We also prove that the Cauchy problem for the equation (1) admits a unique global generalized solution in C2([0, ∞); Wm, p(R) ∩ L∞(R))(m ≥ 0 is an integer,1 ≤ p ≤ ∞) and a unique global classical solution in . [ABSTRACT FROM AUTHOR]

Objectives: To develop a computer-aided diagnosis (CAD) system for distinguishing malignant from benign pulmonary nodules on computed tomography (CT) scans, and to assess whether the diagnostic performance of radiologists with different experiences can be improved with the assistant of CAD., Materials and Methods: A total of 857 malignant nodules from 601 patients and 426 benign nodules from 278 patients were retrospectively collected from four hospitals. In this study, we exploited convolutional neural network in the framework of deep learning to classify whether a nodule was benign or malignant. A total of 745 malignant nodules and 370 benign nodules were used as the training data of our CAD system. The remaining 112 malignant nodules and 56 benign nodules were used as the test data. The participants were two senior chest radiologists, two secondary chest radiologists, and two junior radiology residents. The readers estimated the likelihood of malignancy of pulmonary nodules first without and then with CAD output. Receiver-operating characteristic (ROC) curve was used to evaluate readers' diagnostic performance., Results: When a threshold level of 58% was used to estimate the likelihood of malignancy, the sensitivity, specificity, and diagnostic accuracy values of our CAD scheme alone were 93.8%, 83.9%, and 90.5%, respectively. For all six readers, the mean area under the ROC curve (A z ) values without and with CAD system were 0.913 and 0.938, respectively. For each reader, there is a large difference in A z values that assessed without and with CAD system. With CAD output, the readers made correct changes an average of 15.7 times and incorrect changes an average of 2 times., Conclusion: Our CAD system significantly improved the diagnostic performance of readers regardless of their experience levels for assessment of the likelihood of malignancy of pulmonary nodules., (© 2020 John Wiley & Sons Australia, Ltd.)