1 code implementation • 14 May 2020 • Gerome Vivar, Anees Kazi, Hendrik Burwinkel, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
As a solution, we propose an end-to-end learning of imputation and disease prediction of incomplete medical datasets via Multigraph Geometric Matrix Completion (MGMC).
no code implementations • 31 Mar 2020 • Gerome Vivar, Kamilia Mullakaeva, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians.
no code implementations • 8 May 2019 • Gerome Vivar, Hendrik Burwinkel, Anees Kazi, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
Recently, several works proposed geometric deep learning approaches to solve disease classification, by modeling patients as nodes in a graph, along with graph signal processing of multi-modal features.
no code implementations • 8 May 2019 • Hendrik Burwinkel, Anees Kazi, Gerome Vivar, Shadi Albarqouni, Guillaume Zahnd, Nassir Navab, Seyed-Ahmad Ahmadi
We propose a new network architecture that exploits an inductive end-to-end learning approach for disease classification, where filters from both the CNN and the graph are trained jointly.
no code implementations • 11 Mar 2019 • Anees Kazi, Shayan shekarforoush, S. Arvind krishna, Hendrik Burwinkel, Gerome Vivar, Karsten Kortuem, Seyed-Ahmad Ahmadi, Shadi Albarqouni, Nassir Navab
Geometric deep learning provides a principled and versatile manner for the integration of imaging and non-imaging modalities in the medical domain.
no code implementations • 30 Mar 2018 • Gerome Vivar, Andreas Zwergal, Nassir Navab, Seyed-Ahmad Ahmadi
In this work, we follow up on the idea of modeling multi-modal disease classification as a matrix completion problem, with simultaneous classification and non-linear imputation of features.