EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images
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Title
EAR-UNet: A deep learning-based approach for segmentation of tympanic membranes from otoscopic images
Authors
Keywords
Tympanic membrane segmentation, Unet, Attention gate, Efficientnet, Resnet
Journal
ARTIFICIAL INTELLIGENCE IN MEDICINE
Volume 115, Issue -, Pages 102065
Publisher
Elsevier BV
Online
2021-04-08
DOI
10.1016/j.artmed.2021.102065
References
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- (2020) Junlong Cheng et al. ARTIFICIAL INTELLIGENCE IN MEDICINE
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- Decrease in Pneumococcal Otitis Media Cultures With Concomitant Increased Antibiotic Susceptibility in the Pneumococcal Conjugate Vaccines Era
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- Pediatric otitis media in Fiji: Survey findings 2015
- (2016) Te-Yung Fang et al. INTERNATIONAL JOURNAL OF PEDIATRIC OTORHINOLARYNGOLOGY
- A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI
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- The Diagnosis and Management of Acute Otitis Media
- (2013) A. S. Lieberthal et al. PEDIATRICS
- Incidence and recurrence of acute otitis media in Taiwan's pediatric population
- (2011) Pa-Chun Wang et al. Clinics
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- (2008) T S Ibekwe et al. JOURNAL OF LARYNGOLOGY AND OTOLOGY
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