The correlation of feature identification and category judgments in diagnostic radiology.

Mem Cognit

Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada.

Published: July 1992

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.

Download full-text PDF

Source
http://dx.doi.org/10.3758/bf03210919DOI Listing

Publication Analysis

Top Keywords

feature identification
8
clinical histories
8
difficult films
8
normal abnormal
8
films
6
correlation feature
4
identification category
4
category judgments
4
judgments diagnostic
4
diagnostic radiology
4

Similar Publications

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 PDF

Typhoon localization detection algorithm based on TGE-YOLO.

Sci 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 PDF

A multi-domain feature fusion epilepsy seizure detection method based on spike matching and PLV functional networks.

J 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 PDF

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 PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!