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Deep learning with multi-dimensional medical image data
Citation Link: https://doi.org/10.15480/882.3216
Publikationstyp
Doctoral Thesis
Date Issued
2020-12-18
Sprache
English
Author(s)
Advisor
Referee
Title Granting Institution
Technische Universität Hamburg
Place of Title Granting Institution
Hamburg
Examination Date
2020-12-17
Institut
TORE-DOI
TORE-URI
Citation
Technische Universität Hamburg (2020)
In this work, we explore deep learning model design and application in the context of multi-dimensional data in medical image analysis. A lot of medical image analysis problems come with 3D or even 4D spatio-temporal data that requires appropriate processing. While higher-dimensional processing allows for exploiting a lot of context, model design becomes very challenging due to exponentially increasing model parameters and risk of overfitting. Therefore, we design a variety of deep learning models for low- and high-dimensional data processing, including 1D up to 4D convolutional neural networks, convolutional-recurrent models, and Siamese architectures. Across a large number of applications, we find that using high-dimensional data is often effective when using well-designed deep learning models.
Subjects
medical imaging
Deep learning
machine learning
Optical coherence tomography
Magnetic resonance imaging
DDC Class
004: Informatik
610: Medizin
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