no code implementations • 20 Jul 2021 • Gerda Bortsova, Daniel Bos, Florian Dubost, Meike W. Vernooij, M. Kamran Ikram, Gijs van Tulder, Marleen de Bruijne
To evaluate the method, we compared manual and automatic assessment (computed using ten-fold cross-validation) with respect to 1) the agreement with an independent observer's assessment (available in a random subset of 47 scans); 2) the accuracy in delineating ICAC as judged via blinded visual comparison by an expert; 3) the association with first stroke incidence from the scan date until 2012.
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.
3 code implementations • 18 Jun 2020 • Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger
The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance.
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.
1 code implementation • 4 Nov 2019 • Florian Dubost, Benjamin Collery, Antonin Renaudier, Axel Roc, Nicolas Posocco, Gerda Bortsova, Wiro Niessen, Marleen de Bruijne
For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles.
1 code implementation • 29 Jul 2019 • Shuai Chen, Gerda Bortsova, Antonio Garcia-Uceda Juarez, Gijs van Tulder, Marleen de Bruijne
The reconstruction objective uses an attention mechanism that separates the reconstruction of image areas corresponding to different classes.
no code implementations • 5 Jun 2019 • Florian Dubost, Hieab Adams, Pinar Yilmaz, Gerda Bortsova, Gijs van Tulder, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
For comparison, we modify state-of-the-art methods to compute attention maps for weakly supervised object detection, by using a global regression objective instead of the more conventional classification objective.
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 • 12 Jul 2018 • Florian Dubost, Gerda Bortsova, Hieab Adams, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
The proposed method reached an intraclass correlation coefficient between ground truth and network predictions of 0. 73 on the first task and 0. 84 on the second task, only using between 25 and 30 scans with a single global label per scan for training.
no code implementations • 16 Feb 2018 • Florian Dubost, Hieab Adams, Gerda Bortsova, M. Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI.
no code implementations • 4 Jun 2017 • Gerda Bortsova, Gijs van Tulder, Florian Dubost, Tingying Peng, Nassir Navab, Aad van der Lugt, Daniel Bos, Marleen de Bruijne
In this paper, we propose a method for automatic segmentation of ICAC; the first to our knowledge.
no code implementations • 22 May 2017 • Florian Dubost, Gerda Bortsova, Hieab Adams, Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count.
no code implementations • 26 Aug 2016 • Gerda Bortsova, Michael Sterr, Lichao Wang, Fausto Milletari, Nassir Navab, Anika Böttcher, Heiko Lickert, Fabian Theis, Tingying Peng
A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually.