1 code implementation • 9 Sep 2022 • Sina Amirrajab, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer
We devise three approaches for label manipulation in the latent space of the trained VAE model; i) \textbf{intra-subject synthesis} aiming to interpolate the intermediate slices of a subject to increase the through-plane resolution, ii) \textbf{inter-subject synthesis} aiming to interpolate the geometry and appearance of intermediate images between two dissimilar subjects acquired with different scanner vendors, and iii) \textbf{pathology synthesis} aiming to synthesize a series of pseudo-pathological synthetic subjects with characteristics of a desired heart disease.
1 code implementation • 12 Jun 2017 • Veronika Cheplygina, Pim Moeskops, Mitko Veta, Behdad Dasht Bozorg, Josien Pluim
Supervised learning is ubiquitous in medical image analysis.
no code implementations • MIDL 2019 • Samaneh Abbasi-Sureshjani, Sina Amirrajab, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer
Using the parameterized motion model of the XCAT heart, we generate labels for 25 time frames of the heart for one cardiac cycle at 18 locations for the short axis view.
no code implementations • 27 Jul 2020 • Sina Amirrajab, Samaneh Abbasi-Sureshjani, Yasmina Al Khalil, Cristian Lorenz, Juergen Weese, Josien Pluim, Marcel Breeuwer
Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.
no code implementations • 1 Jun 2021 • Juul P. A. van Boxtel, Vincent R. J. Vousten, Josien Pluim, Nastaran Mohammadian Rad
In this paper, we propose a hybrid model consisting of a classification model followed by a segmentation model to segment BP nerve regions in ultrasound images.
no code implementations • 3 Jun 2022 • Seyed Mostafa Kia, Nastaran Mohammadian Rad, Daniel van Opstal, Bart van Schie, Andre F. Marquand, Josien Pluim, Wiepke Cahn, Hugo G. Schnack
In this method, there is no need to remove or impute the missing values; instead, the missing values are treated as a new source of information (representing what we do not know).
no code implementations • 9 Aug 2022 • Sina Amirrajab, Yasmina Al Khalil, Cristian Lorenz, Jurgen Weese, Josien Pluim, Marcel Breeuwer
There has been considerable interest in the MR physics-based simulation of a database of virtual cardiac MR images for the development of deep-learning analysis networks.
1 code implementation • 8 Apr 2024 • Hassan Keshvarikhojasteh, Josien Pluim, Mitko Veta
This paper introduces MAD-MIL, a Multi-head Attention-based Deep Multiple Instance Learning model, designed for weakly supervised Whole Slide Images (WSIs) classification in digital pathology.