Tommy Löfstedt

According to our database, Tommy Löfstedt authored at least 26 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect.

Minh H. Vu, Anders Garpebring, Tufve Nyholm, Tommy Löfstedt

Trans. Mach. Learn. Res., 2024

A Cost-Aware Approach to Adversarial Robustness in Neural Networks.

Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth

CoRR, 2024

A Correlation- and Mean-Aware Loss Function and Benchmarking Framework to Improve GAN-based Tabular Data Synthesis.

Minh H. Vu, Daniel Edler, Carl Wibom, Tommy Löfstedt, Beatrice Melin, Martin Rosvall

CoRR, 2024

Synthesizing multi-log grasp poses.

Arvid Fälldin, Erik Wallin, Tommy Löfstedt, Martin Servin

CoRR, 2024

A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks.

Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth

CoRR, 2024

2023

Improving MR image quality with a multi-task model, using convolutional losses.

Attila Simkó, Simone Ruiter, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Mikael Bylund, Joakim Jonsson

BMC Medical Imaging, December, 2023

Safety-critical computer vision: an empirical survey of adversarial evasion attacks and defenses on computer vision systems.

Charles Meyers, Tommy Löfstedt, Erik Elmroth

Artif. Intell. Rev., October, 2023

LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space.

Lorenzo Tronchin, Minh H. Vu, Paolo Soda, Tommy Löfstedt

CoRR, 2023

Reproducibility of the Methods in Medical Imaging with Deep Learning.

Attila Simkó, Anders Garpebring, Joakim Jonsson, Tufve Nyholm, Tommy Löfstedt

Proceedings of the Medical Imaging with Deep Learning, 2023

2022

A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation.

Minh H. Vu, Gabriella Norman, Tufve Nyholm, Tommy Löfstedt

IEEE Trans. Medical Imaging, 2022

MRI bias field correction with an implicitly trained CNN.

Attila Simkó, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson

Proceedings of the International Conference on Medical Imaging with Deep Learning, 2022

2021

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results.

Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard McKinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil S. Nalawade, Chandan Ganesh, Benjamin C. Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Alexandra Daza, Catalina Gómez Caballero, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Raynaud, Yuanhan Mo, Elsa D. Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Linmin Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Verónica Vilaplana, Hugh McHugh, Gonzalo D. Maso Talou, Alan Wang, Jay B. Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Thumbavanam Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicolas Boutry, Alexis Huard, Lasitha Vidyaratne, Md Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Élodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Lladó, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas J. Tustison, Craig H. Meyer, Nisarg A. Shah, Sanjay N. Talbar, Marc-André Weber, Abhishek Mahajan, András Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko, Daniel S. Marcus, Aikaterini Kotrotsou, Rivka Colen, John B. Freymann, Justin S. Kirby, Christos Davatzikos, Bjoern H. Menze, Spyridon Bakas, Yarin Gal, Tal Arbel

CoRR, 2021

Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning.

Attila Tibor Simkó, Tommy Löfstedt, Anders Garpebring, Mikael Bylund, Tufve Nyholm, Joakim Jonsson

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

2020

A Question-Centric Model for Visual Question Answering in Medical Imaging.

Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman

IEEE Trans. Medical Imaging, 2020

Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation.

Minh H. Vu, Tufve Nyholm, Tommy Löfstedt

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020

2019

Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation.

Minh H. Vu, Guus Grimbergen, Tufve Nyholm, Tommy Löfstedt

CoRR, 2019

End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation.

Minh H. Vu, Guus Grimbergen, Attila Simkó, Tufve Nyholm, Tommy Löfstedt

CoRR, 2019

Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer's Disease.

Nicolas Guigui, Cathy Philippe, Arnaud Gloaguen, Slim Karkar, Vincent Guillemot, Tommy Löfstedt, Vincent Frouin

Proceedings of the 16th IEEE International Symposium on Biomedical Imaging, 2019

Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain.

Minh H. Vu, Raphael Sznitman, Tufve Nyholm, Tommy Löfstedt

Proceedings of the Working Notes of CLEF 2019, 2019

TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks.

Minh H. Vu, Tufve Nyholm, Tommy Löfstedt

Proceedings of the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019

2018

Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty.

Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Renaud Jardri, Thomas Fovet, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay

IEEE Trans. Medical Imaging, 2018

Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging.

Fouad Hadj-Selem, Tommy Löfstedt, Elvis Dohmatob, Vincent Frouin, Mathieu Dubois, Vincent Guillemot, Edouard Duchesnay

IEEE Trans. Medical Imaging, 2018

Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity.

Amicie de Pierrefeu, Tommy Löfstedt, Charles Laidi, Fouad Hadj-Selem, Marion Leboyer, Philippe Ciuciu, Josselin Houenou, Edouard Duchesnay

Proceedings of the 2018 International Workshop on Pattern Recognition in Neuroimaging, 2018

2014

Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI.

Mathieu Dubois, Fouad Hadj-Selem, Tommy Löfstedt, Matthieu Perrot, Clara Fischer, Vincent Frouin, Edouard Duchesnay

Proceedings of the International Workshop on Pattern Recognition in Neuroimaging, 2014

2012

Dynamic ultrasound imaging - A multivariate approach for the analysis and comparison of time-dependent musculoskeletal movements.

Tommy Löfstedt, Olof Ahnlund, Michael Peolsson, Johan Trygg

BMC Medical Imaging, 2012

2010

Modelling human musculoskeletal functional movements using ultrasound imaging.

Michael Peolsson, Tommy Löfstedt, Susanna Vogt, Hans Stenlund, Anton Arndt, Johan Trygg

BMC Medical Imaging, 2010