@misc phdthesis{11420_8296,
author = {N. Gessert},
title = {Deep learning with multi-dimensional medical image data.},
journal = {TUHH Open Research.},
year = {2020},
month = {Dec},
publisher = {TUHH Open Research:},
address = {Hamburg, Germany},
booktitle = {TUHH Open Research},
doi = {10.15480/882.3216},
url = {http://hdl.handle.net/11420/8296},
type = {doctoralThesis},
school = {Technische Universität Hamburg},
keywords = {medical imaging; Deep learning; machine learning; Optical coherence tomography; Magnetic resonance imaging;},
abstract = {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.}
}

@COMMENT{Bibtex file generated on 2026-5-28 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/mtec/publications/2024-2020 }