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Deep learning for paranasal anomaly classification
Citation Link: https://doi.org/10.15480/882.14833
Publikationstyp
Doctoral Thesis
Date Issued
2025
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2025-02-11
TORE-DOI
Citation
Technische Universität Hamburg (2025)
The susceptibility of the paranasal sinuses to allergic and non-allergic infections complicates diagnosis as symptoms vary from mucosal thickening to polypoid masses. Previous manual analysis is resource intensive and leads to clinician fatigue. Computer-aided diagnosis (CAD) can optimize this process.
While deep learning has made great strides in medical image analysis, the study of paranasal abnormalities has remained limited. This work proposes different deep learning approaches to classify maxillary sinus opacities, including unsupervised learning, self-supervised learning, and hybrid architectures of CNNs and transformers.
While deep learning has made great strides in medical image analysis, the study of paranasal abnormalities has remained limited. This work proposes different deep learning approaches to classify maxillary sinus opacities, including unsupervised learning, self-supervised learning, and hybrid architectures of CNNs and transformers.
Subjects
medical imaging
paranasal anomaly
deep learning
self supervised learning
maxillary sinus
machine learning
DDC Class
616: Deseases
617: Surgery, Regional Medicine, Dentistry, Ophthalmology, Otology, Audiology
006.3: Artificial Intelligence
004: Computer Sciences
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Dissertation Camera Ready-compressed-output-2.pdf
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3.29 MB
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