Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning

Background There is progress to be made in building artificially intelligent systems to detect abnormalities that are not only accurate but can handle the true breadth of findings that radiologists encounter in body (chest, abdomen, and pelvis) computed tomography (CT). Currently, the major bottlene...

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Veröffentlicht in:	BMC medical informatics and decision making Jg. 22; H. 1; S. 102 - 12
Hauptverfasser:	D’Anniballe, Vincent M., Tushar, Fakrul Islam, Faryna, Khrystyna, Han, Songyue, Mazurowski, Maciej A., Rubin, Geoffrey D., Lo, Joseph Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 15.04.2022 Springer Nature B.V BMC
Schlagworte:	Abdomen Abnormalities Accuracy Algorithms Annotations Attention RNN Automation Chest Classification Classifiers Computed tomography Datasets Deep Learning Dictionaries Disease Disease control Diseases Embedding Gallbladder Health Informatics Humans Information Systems and Communication Service Keywords Labels Liver Logic Machine learning Management of Computing and Information Systems Medicine Medicine & Public Health Natural language processing Neural networks Neural Networks, Computer Organs Patient assessment Pelvis Pelvis - diagnostic imaging Performance evaluation Pleura Radiology Recurrent neural networks Report labeling Rule-based algorithm Semantics Tomography, X-Ray Computed Training Weak supervision Rule-based algorithm Weak supervision Natural language processing Attention RNN Computed tomography Report labeling
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
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