Regressive vision transformer for dog cardiomegaly assessment.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
17 Jan 2024
17 Jan 2024
Historique:
received:
28
04
2023
accepted:
14
12
2023
medline:
18
1
2024
pubmed:
18
1
2024
entrez:
17
1
2024
Statut:
epublish
Résumé
Cardiac disease is one of the leading causes of death in dogs. Automatic cardiomegaly detection has great significance in helping clinicians improve the accuracy of the diagnosis process. Deep learning methods show promising results in improving cardiomegaly classification accuracy, while they are still not widely applied in clinical trials due to the difficulty in mapping predicted results with input radiographs. To overcome these challenges, we first collect large-scale dog heart X-ray images. We then develop a dog heart labeling tool and apply a few-shot generalization strategy to accelerate the label speed. We also develop a regressive vision transformer model with an orthogonal layer to bridge traditional clinically used VHS metric with deep learning models. Extensive experimental results demonstrate that the proposed model achieves state-of-the-art performance.
Identifiants
pubmed: 38233422
doi: 10.1038/s41598-023-50063-x
pii: 10.1038/s41598-023-50063-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1539Informations de copyright
© 2023. The Author(s).
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