@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 Universit├Ąt 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 2021-1-18 with typo3 si_bibtex plugin. Data from /mtec/publications/2020.html }