no code implementations • 25 Mar 2024 • Nikita Durasov, Doruk Oner, Jonathan Donier, Hieu Le, Pascal Fua
Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance.
1 code implementation • 14 Jul 2022 • Doruk Oner, Hussein Osman, Mateusz Kozinski, Pascal Fua
Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes.
1 code implementation • 6 Dec 2021 • Doruk Oner, Leonardo Citraro, Mateusz Koziński, Pascal Fua
Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks.
no code implementations • 12 Oct 2021 • Doruk Oner, Adélie Garin, Mateusz Koziński, Kathryn Hess, Pascal Fua
Persistent Homology (PH) has been successfully used to train networks to detect curvilinear structures and to improve the topological quality of their results.
no code implementations • 29 Sep 2021 • Doruk Oner, Adélie Garin, Mateusz Kozinski, Kathryn Hess, Pascal Fua
Persistent Homologies have been successfully used to increase the performance of deep networks trained to detect curvilinear structures and to improve the topological quality of the results.
1 code implementation • 15 Sep 2020 • Doruk Oner, Mateusz Koziński, Leonardo Citraro, Nathan C. Dadap, Alexandra G. Konings, Pascal Fua
The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image.