@conference{bargsten-Wendebourg-2019, Author = {L. Bargsten and M. Wendebourg and A. Schlaefer}, Title = {Data Representations for Segmentation of Vascular Structures Using Convolutional Neural Networks with U-Net Architecture}, Year = {2019}, Pages = {Accepted}, Booktitle = {In Proc. IEEE Engineering in Medicine and Biology Society (EMBC'19) Berlin, Germany}, Url = {https://embs.papercept.net/conferences/conferences/EMBC19/program/EMBC19_ContentListWeb_2.html}, Abstract = {Autism spectrum disorder (ASD) is associated with behavioral and communication problems. Often, functional magnetic resonance imaging (fMRI) is used to detect and characterize brain changes related to the disorder. Recently, machine learning methods have been employed to reveal new patterns by trying to classify ASD from spatio-temporal fMRI images. Typically, these methods have either focused on temporal or spatial information processing. Instead, we propose a 4D spatio-temporal deep learning approach for ASD classification where we jointly learn from spatial and temporal data. We employ 4D convolutional neural networks and convolutional-recurrent models which outperform a previous approach with an F1-score of 0.71 compared to an F1-score of 0.65} } @COMMENT{Bibtex file generated on 2019-8-26 with typo3 si_bibtex plugin. Data from /mtec/publications/2017-2013.html }