Expert and novice radiologists were given films accompanied by clinical histories that supported a diagnosis either of bronchiolitis or of normal. To provide a plausible task context, some films were radiologically unambiguous and were accompanied by histories consistent with them. For a set of radiologically difficult films from confirmed normal or bronchiolitis patients, fictitious normal or abnormal histories were counterbalanced with the films. The clinical histories affected ratings both of diagnosis and of features present on the difficult films. Thus, uncertainty about individual features evidently was affected by history, and features did not act as an independent source of information. The dependence of feature calls on an overall judgment was also suggested by intra-observer agreement in another study in which an explicit diagnosis was not requested. It is unclear whether the history increased discrimination between normal and abnormal films, or indiscriminately added evidence for or against the disease. Factors are discussed that make it appropriate for feature identification to be partially dependent on category identification.
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http://dx.doi.org/10.3758/bf03210919 | DOI Listing |
Sci Rep
January 2025
General Surgery Department, Jiangsu University Affiliated People's Hospital, Zhenjiang, 212000, China.
Crohn's disease (CD) is a chronic inflammatory bowel disease with an unknown etiology. Ubiquitination plays a significant role in the pathogenesis of CD. This study aimed to explore the functional roles of ubiquitination-related genes in CD.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
To address the problems of complex cloud features in satellite cloud maps, inaccurate typhoon localization, and poor target detection accuracy, this paper proposes a new typhoon localization algorithm, named TGE-YOLO. It is based on the YOLOv8n model with excellent high-low feature fusion capability and innovatively achieves the organic combination of feature fusion, computational efficiency, and localization accuracy. Firstly, the TFAM_Concat module is creatively designed in the neck network, which comprehensively utilizes the detailed information of shallow features and the semantic information of deeper features, enhancing the fusion ability of features at each layer.
View Article and Find Full Text PDFJ Neural Eng
January 2025
Hangzhou Dianzi University, School of Automation, Hangzhou Dianzi University, Hangzhou 310052, China, Hangzhou, Zhejiang, 310018, CHINA.
The identification of spikes, as a typical characteristic wave of epilepsy, is crucial for diagnosing and locating the epileptogenic region. The traditional seizure detection methods lack spike features and have low sample richness. This paper proposes a seizure detection method with spike-based phase locking value (PLV) functional brain networks and multi-domain fused features.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
Department of Experimental Psychology, Justus Liebig University Giessen, Giessen, Germany.
The human visual system possesses a remarkable ability to detect and process faces across diverse contexts, including the phenomenon of face pareidolia--seeing faces in inanimate objects. Despite extensive research, it remains unclear why the visual system employs such broadly tuned face detection capabilities. We hypothesized that face pareidolia results from the visual system's optimization for recognizing both faces and objects.
View Article and Find Full Text PDFPLoS One
January 2025
School of Information and Communication Engineering, Beijing University of Information Science and Technology, Bei Jing City, China.
To enhance the intelligent classification of computer vulnerabilities and improve the efficiency and accuracy of network security management, this study delves into the application of a comprehensive classification system that integrates the Memristor Neural Network (MNN) and an improved Temporal Convolutional Neural Network (TCNN) in network security management. This system not only focuses on the precise classification of vulnerability data but also emphasizes its core role in strengthening the network security management framework. Firstly, the study designs and implements a neural network model based on memristors.
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