Search Results for author: Mitko Veta

Found 25 papers, 9 papers with code

Optimized Automated Cardiac MR Scar Quantification with GAN-Based Data Augmentation

1 code implementation27 Sep 2021 Didier R. P. R. M. Lustermans, Sina Amirrajab, Mitko Veta, Marcel Breeuwer, Cian M. Scannell

The mean DSC per-subject on the challenge test set, for the cascaded pipeline augmented by synthetic generated data, was 0. 86 (0. 03) and 0. 67 (0. 29) for myocardium and scar, respectively.

Data Augmentation

Quantifying the Scanner-Induced Domain Gap in Mitosis Detection

1 code implementation30 Mar 2021 Marc Aubreville, Christof Bertram, Mitko Veta, Robert Klopfleisch, Nikolas Stathonikos, Katharina Breininger, Natalie ter Hoeve, Francesco Ciompi, Andreas Maier

Hypothesizing that the scanner device plays a decisive role in this effect, we evaluated the susceptibility of a standard mitosis detection approach to the domain shift introduced by using a different whole slide scanner.

Mitosis Detection

Corneal Pachymetry by AS-OCT after Descemet's Membrane Endothelial Keratoplasty

no code implementations15 Feb 2021 Friso G. Heslinga, Ruben T. Lucassen, Myrthe A. van den Berg, Luuk van der Hoek, Josien P. W. Pluim, Javier Cabrerizo, Mark Alberti, Mitko Veta

In this research, deep learning is used to automatically delineate the corneal interfaces and measure corneal thickness with high accuracy in post-DMEK AS-OCT B-scans.

Physics-informed neural networks for myocardial perfusion MRI quantification

1 code implementation25 Nov 2020 Rudolf L. M. van Herten, Amedeo Chiribiri, Marcel Breeuwer, Mitko Veta, Cian M. Scannell

This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters.

Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology

no code implementations26 Aug 2020 Maxime W. Lafarge, Josien P. W. Pluim, Mitko Veta

However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use.

Representation Learning

Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis

1 code implementation20 Feb 2020 Maxime W. Lafarge, Erik J. Bekkers, Josien P. W. Pluim, Remco Duits, Mitko Veta

This study is focused on histopathology image analysis applications for which it is desirable that the arbitrary global orientation information of the imaged tissues is not captured by the machine learning models.

Breast Tumour Classification Colorectal Gland Segmentation: +4

Intensity augmentation for domain transfer of whole breast segmentation in MRI

no code implementations5 Sep 2019 Linde S. Hesse, Grey Kuling, Mitko Veta, Anne L. Martel

Our results show that using intensity augmentation in addition to geometric augmentation is a suitable method to overcome the intensity domain shift and we expect it to be useful for a wide range of segmentation tasks.

Domain Adaptation Style Transfer

Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI

no code implementations27 Jul 2019 Cian M. Scannell, Piet van den Bosch, Amedeo Chiribiri, Jack Lee, Marcel Breeuwer, Mitko Veta

The quantification of myocardial perfusion MRI has the potential to provide a fast, automated and user-independent assessment of myocardial ischaemia.

Bayesian Inference

Inferring a Third Spatial Dimension from 2D Histological Images

no code implementations10 Jan 2018 Maxime W. Lafarge, Josien P. W. Pluim, Koen A. J. Eppenhof, Pim Moeskops, Mitko Veta

Histological images are obtained by transmitting light through a tissue specimen that has been stained in order to produce contrast.

Data Augmentation

Adversarial training and dilated convolutions for brain MRI segmentation

no code implementations11 Jul 2017 Pim Moeskops, Mitko Veta, Maxime W. Lafarge, Koen A. J. Eppenhof, Josien P. W. Pluim

To this end, we include an additional loss function that motivates the network to generate segmentations that are difficult to distinguish from manual segmentations.

MRI segmentation Semantic Segmentation

Cutting out the middleman: measuring nuclear area in histopathology slides without segmentation

no code implementations20 Jun 2016 Mitko Veta, Paul J. van Diest, Josien P. W. Pluim

We hypothesize that given an image of a tumor region with known nuclei locations, the area of the individual nuclei and region statistics such as the MNA can be reliably computed directly from the image data by employing a machine learning model, without the intermediate step of nuclei segmentation.

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