Early screening of miliary tuberculosis with tuberculous meningitis based on few-shot learning with multiple windows and feature granularities.
Chest CT Images
Computer-Aided Diagnostic
Few-shot Learning
Miliary Tuberculosis with Tuberculous Meningitis
Multiple Windows and Feature Granularities Module
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
09 10 2024
09 10 2024
Historique:
received:
24
02
2024
accepted:
03
10
2024
medline:
10
10
2024
pubmed:
10
10
2024
entrez:
9
10
2024
Statut:
epublish
Résumé
Tuberculous meningitis (TBM) is a fatal tuberculosis caused by a large number of Mycobacterium tuberculosis (M. tuberculosis) spread by blood flow, with a case fatality rate of more than 50%. It is one of the most serious complications of miliary tuberculosis (MT), whose incidence is closely related to MT. If doctors can provide early diagnosis and active treatment for TBM, the case fatality rate will be significantly reduced. At present, there is a lack of methods to predict the progression of MT to TBM in clinic. To explore whether MT cases will experience TBM, we propose an early screening model of miliary tuberculosis with tuberculous meningitis (MT-TBM) based on few-shot learning with multiple windows and feature granularities (MWFG). This model aims to screen potential TBM cases through chest computerized tomography (CT) images of MT cases. Chest CT is a routine examination for MT cases. The MWFG module can extract more comprehensive features from a set of CT images of each MT case. The softmax classifier with adaptive regularization is trained on the cooperation of support set and query set, which can effectively prevent overfitting. Experiments on a dataset of 40 MT cases with chest CT images established by the medical records demonstrate that our proposed model achieves state-of-the-art performance in the early screening of MT-TBM. It can establish the connection between MT and MT-TBM through chest CT images of MT cases. The early screening model of MT-TBM based on few-shot learning with MWFG fills the research gap in computer-aided predicting TBM and has certain clinical effects. This research can provide some reference for clinicians in early diagnosis of MT-TBM and help clinicians in the early prevention and treatment of TBM for MT patients.
Identifiants
pubmed: 39384848
doi: 10.1038/s41598-024-75253-z
pii: 10.1038/s41598-024-75253-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
23620Informations de copyright
© 2024. The Author(s).
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