Aschwanden, Fabian

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Number of items: 5.

Journal Article

McKinley, Richard; Wepfer, Rik; Aschwanden, Fabian; Grunder, Lorenz; Muri, Raphaela; Rummel, Christian; Verma, Rajeev; Weisstanner, Christian; Reyes, Mauricio; Salmen, Anke; Chan, Andrew; Wagner, Franca; Wiest, Roland (2021). Simultaneous lesion and brain segmentation in multiple sclerosis using deep neural networks. Scientific reports, 11(1), p. 1087. Springer Nature 10.1038/s41598-020-79925-4

McKinley, Richard; Wepfer, Rik; Grunder, Lorenz; Aschwanden, Fabian; Fischer, Tim; Friedli, Christoph; Muri, Raphaela; Rummel, Christian; Verma, Rajeev Kumar; Weisstanner, Christian; Wiestler, Benedikt; Berger, Christoph; Eichinger, Paul; Muehlau, Mark; Reyes, Mauricio; Salmen, Anke; Chan, Andrew; Wiest, Roland; Wagner, Franca (2020). Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. NeuroImage: Clinical, 25, p. 102104. Elsevier 10.1016/j.nicl.2019.102104

McKinley, Richard; Wepfer, Rik; Aschwanden, Fabian; Grunder, Lorenz; Muri, Raphaela; Rummel, Christian; Verma, Rajeev; Weisstanner, Christian; Reyes, Mauricio; Salmen, Anke; Chan, Andrew; Wagner, Franca; Wiest, Roland (2020). Robustness of Simultaneous Lesion and Neuroanatomy Segmentation in Multiple Sclerosis Using Deep Neural Networks (In Press). SSRN Electronic Journal Elsevier 10.2139/ssrn.3529469

Rummel, Christian; Aschwanden, Fabian; McKinley, Richard; Wagner, Franca; Salmen, Anke; Chan, Andrew; Wiest, Roland (2018). A Fully Automated Pipeline for Normative Atrophy in Patients with Neurodegenerative Disease. Frontiers in neurology, 8, p. 727. Frontiers Media S.A. 10.3389/fneur.2017.00727

Working Paper

McKinley, Richard; Grunder, Lorenz; Wepfer, Rik; Aschwanden, Fabian; Fischer, Tim; Friedli, Christoph; Muri, Raphaela; Rummel, Christian; Verma, Rajeev; Weisstanner, Christian; Reyes, Mauricio; Salmen, Anke; Chan, Andrew; Wiest, Roland; Wagner, Franca (2019). Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence (arXiv). Cornell University

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