Search Results for author: Mohsen Ghafoorian

Found 17 papers, 3 papers with code

InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning

no code implementations26 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.

Computational Efficiency Multi-Task Learning

PVP: Personalized Video Prior for Editable Dynamic Portraits using StyleGAN

no code implementations29 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.

Face Generation

3D Distillation: Improving Self-Supervised Monocular Depth Estimation on Reflective Surfaces

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.

Monocular Depth Estimation

DG-Recon: Depth-Guided Neural 3D Scene Reconstruction

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.

3D Reconstruction 3D Scene Reconstruction

Multi-Task Edge Prediction in Temporally-Dynamic Video Graphs

no code implementations6 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.

Graph Attention object-detection +2

3D Convolutional Neural Networks Image Registration Based on Efficient Supervised Learning from Artificial Deformations

1 code implementation27 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.

Image Registration

I Bet You Are Wrong: Gambling Adversarial Networks for Structured Semantic Segmentation

no code implementations7 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.

Segmentation Semantic Segmentation

Comparison of U-net-based Convolutional Neural Networks for Liver Segmentation in CT

no code implementations9 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.

Liver Segmentation

Student Beats the Teacher: Deep Neural Networks for Lateral Ventricles Segmentation in Brain MR

no code implementations15 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.


Transfer Learning for Domain Adaptation in MRI: Application in Brain Lesion Segmentation

no code implementations25 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?

Domain Adaptation Lesion Segmentation +1

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