no code implementations • 22 Mar 2024 • Florian Langer, Jihong Ju, Georgi Dikov, Gerhard Reitmayr, Mohsen Ghafoorian
In contrast to previous works, we directly predict alignment parameters and shape embeddings.
no code implementations • 26 Feb 2024 • Babak Ehteshami Bejnordi, Gaurav Kumar, Amelie Royer, Christos Louizos, Tijmen Blankevoort, Mohsen Ghafoorian
In this work, we propose \textit{InterroGate}, a novel multi-task learning (MTL) architecture designed to mitigate task interference while optimizing inference computational efficiency.
no code implementations • 29 Jun 2023 • Kai-En Lin, Alex Trevithick, Keli Cheng, Michel Sarkis, Mohsen Ghafoorian, Ning Bi, Gerhard Reitmayr, Ravi Ramamoorthi
In this work, our goal is to take as input a monocular video of a face, and create an editable dynamic portrait able to handle extreme head poses.
no code implementations • ICCV 2023 • Jihong Ju, Ching Wei Tseng, Oleksandr Bailo, Georgi Dikov, Mohsen Ghafoorian
A key challenge in neural 3D scene reconstruction from monocular images is to fuse features back projected from various views without any depth or occlusion information.
no code implementations • ICCV 2023 • Xuepeng Shi, Georgi Dikov, Gerhard Reitmayr, Tae-Kyun Kim, Mohsen Ghafoorian
Self-supervised monocular depth estimation (SSMDE) aims at predicting the dense depth maps of monocular images, by learning to minimize a photometric loss using spatially neighboring image pairs during training.
no code implementations • 6 Dec 2022 • Osman Ülger, Julian Wiederer, Mohsen Ghafoorian, Vasileios Belagiannis, Pascal Mettes
In such temporally-dynamic graphs, a core problem is inferring the future state of spatio-temporal edges, which can constitute multiple types of relations.
1 code implementation • 9 Nov 2020 • Erik Stammes, Tom F. H. Runia, Michael Hofmann, Mohsen Ghafoorian
Semantic segmentation is a task that traditionally requires a large dataset of pixel-level ground truth labels, which is time-consuming and expensive to obtain.
Ranked #25 on Semantic Segmentation on PASCAL VOC 2012 val
Weakly supervised Semantic Segmentation Weakly-Supervised Semantic Segmentation
1 code implementation • 27 Aug 2019 • Hessam Sokooti, Bob de Vos, Floris Berendsen, Mohsen Ghafoorian, Sahar Yousefi, Boudewijn P. F. Lelieveldt, Ivana Isgum, Marius Staring
We propose a supervised nonrigid image registration method, trained using artificial displacement vector fields (DVF), for which we propose and compare three network architectures.
no code implementations • 7 Aug 2019 • Laurens Samson, Nanne van Noord, Olaf Booij, Michael Hofmann, Efstratios Gavves, Mohsen Ghafoorian
Adversarial training has been recently employed for realizing structured semantic segmentation, in which the aim is to preserve higher-level scene structural consistencies in dense predictions.
1 code implementation • 1 Apr 2019 • Hugo J. Kuijf, J. Matthijs Biesbroek, Jeroen de Bresser, Rutger Heinen, Simon Andermatt, Mariana Bento, Matt Berseth, Mikhail Belyaev, M. Jorge Cardoso, Adrià Casamitjana, D. Louis Collins, Mahsa Dadar, Achilleas Georgiou, Mohsen Ghafoorian, Dakai Jin, April Khademi, Jesse Knight, Hongwei Li, Xavier Lladó, Miguel Luna, Qaiser Mahmood, Richard McKinley, Alireza Mehrtash, Sébastien Ourselin, Bo-yong Park, HyunJin Park, Sang Hyun Park, Simon Pezold, Elodie Puybareau, Leticia Rittner, Carole H. Sudre, Sergi Valverde, Verónica Vilaplana, Roland Wiest, Yongchao Xu, Ziyue Xu, Guodong Zeng, Jian-Guo Zhang, Guoyan Zheng, Christopher Chen, Wiesje van der Flier, Frederik Barkhof, Max A. Viergever, Geert Jan Biessels
Segmentation methods had to be containerized and submitted to the challenge organizers.
no code implementations • 9 Oct 2018 • Hans Meine, Grzegorz Chlebus, Mohsen Ghafoorian, Itaru Endo, Andrea Schenk
Various approaches for liver segmentation in CT have been proposed: Besides statistical shape models, which played a major role in this research area, novel approaches on the basis of convolutional neural networks have been introduced recently.
no code implementations • 14 Jun 2018 • Mohsen Ghafoorian, Cedric Nugteren, Nóra Baka, Olaf Booij, Michael Hofmann
Convolutional neural networks have been successfully applied to semantic segmentation problems.
Ranked #15 on Lane Detection on TuSimple
no code implementations • 15 Jan 2018 • Mohsen Ghafoorian, Jonas Teuwen, Rashindra Manniesing, Frank-Erik de Leeuw, Bram van Ginneken, Nico Karssemeijer, Bram Platel
To show this, we use noisy segmentation labels generated by a conventional region growing algorithm to train a deep network for lateral ventricle segmentation.
no code implementations • 25 Feb 2017 • Mohsen Ghafoorian, Alireza Mehrtash, Tina Kapur, Nico Karssemeijer, Elena Marchiori, Mehran Pesteie, Charles R. G. Guttmann, Frank-Erik de Leeuw, Clare M. Tempany, Bram van Ginneken, Andriy Fedorov, Purang Abolmaesumi, Bram Platel, William M. Wells III
In this study, we aim to answer the following central questions regarding domain adaptation in medical image analysis: Given a fitted legacy model, 1) How much data from the new domain is required for a decent adaptation of the original network?
no code implementations • 19 Feb 2017 • Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A. W. M. van der Laak, Bram van Ginneken, Clara I. Sánchez
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images.
no code implementations • 24 Oct 2016 • Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
no code implementations • 16 Oct 2016 • Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara Sanchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.