no code implementations • 23 Jul 2018 • Gerda Bortsova, Florian Dubost, Silas Ørting, Ioannis Katramados, Laurens Hogeweg, Laura Thomsen, Mathilde Wille, Marleen de Bruijne
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue.
no code implementations • 25 Feb 2019 • Silas Ørting, Andrew Doyle, Arno van Hilten, Matthias Hirth, Oana Inel, Christopher R. Madan, Panagiotis Mavridis, Helen Spiers, Veronika Cheplygina
Despite the growing popularity of this approach, there has not yet been a comprehensive literature review to provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis.
1 code implementation • 25 Sep 2020 • Raghavendra Selvan, Silas Ørting, Erik B. Dam
The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets.
1 code implementation • 13 Nov 2020 • Raghavendra Selvan, Silas Ørting, Erik B Dam
The recently introduced locally orderless tensor network (LoTeNet) for supervised image classification uses matrix product state (MPS) operations on grids of transformed image patches.