Automatic classification of canine thoracic radiographs using deep learning
Abstract The interpretation of thoracic radiographs is a challenging and error-prone task for veterinarians. Despite recent advancements in machine learning and computer vision, the development of computer-aided diagnostic systems for radiographs remains a challenging and unsolved problem, particula...
Enregistré dans:
Auteurs principaux: | Tommaso Banzato, Marek Wodzinski, Silvia Burti, Valentina Longhin Osti, Valentina Rossoni, Manfredo Atzori, Alessandro Zotti |
---|---|
Format: | article |
Langue: | EN |
Publié: |
Nature Portfolio
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/da5a472551f64eec84e60932f09ad09d |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
par: Silvia Burti, et autres
Publié: (2021) -
Corrigendum: Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases
par: Silvia Burti, et autres
Publié: (2021) -
Deep learning for classification of pediatric chest radiographs by WHO's standardized methodology.
par: Yiyun Chen, et autres
Publié: (2021) -
Classification of caries in third molars on panoramic radiographs using deep learning
par: Shankeeth Vinayahalingam, et autres
Publié: (2021) -
Deep learning approach for automatic landmark detection and alignment analysis in whole-spine lateral radiographs
par: Yu-Cheng Yeh, et autres
Publié: (2021)