no code implementations • 31 Mar 2021 • Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne
Previous studies have shown that it is possible to adversarially manipulate automated segmentations produced by neural networks in a targeted manner in the white-box attack setting.
1 code implementation • 11 Jun 2020 • Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein, Florian Dubost, Ioannis Katramados, Laurens Hogeweg, Bart Liefers, Bram van Ginneken, Josien P. W. Pluim, Mitko Veta, Clara I. Sánchez, Marleen de Bruijne
Firstly, we study the effect of weight initialization (ImageNet vs. random) on the transferability of adversarial attacks from the surrogate model to the target model.
no code implementations • 4 Nov 2019 • Gerda Bortsova, Florian Dubost, Laurens Hogeweg, Ioannis Katramados, Marleen de Bruijne
In this paper, we propose a novel semi-supervised method that, in addition to supervised learning on labeled training images, learns to predict segmentations consistent under a given class of transformations on both labeled and unlabeled images.
no code implementations • 28 Jan 2019 • Felix Stephenson, Toby Breckon, Ioannis Katramados
In this work we use the novel Dense Gradient Based Features (DeGraF) as the input to a sparse-to-dense optical flow scheme.
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.