Modelling human musculoskeletal functional movements using ultrasound imaging. - Michael Peolsson; Tommy Löfstedt; Susanna Vogt; Hans Stenlund; Anton Arndt; Johan Trygg (BMC Medical Imaging, 2010)

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)

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 (PRNI, 2014)

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 (PRNI, 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)

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)

TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks. - Minh H. Vu; Tufve Nyholm; Tommy Löfstedt (BrainLes@MICCAI (1), 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 (CLEF (Working Notes), 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 (ISBI, 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)

Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation. - Minh H. Vu; Guus Grimbergen; Tufve Nyholm; Tommy Löfstedt (CoRR, 2019)

Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation. - Minh H. Vu; Tufve Nyholm; Tommy Löfstedt (BrainLes@MICCAI (1), 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)

Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning. - Attila Tibor Simkó; Tommy Löfstedt; Anders Garpebring; Mikael Bylund; Tufve Nyholm; Joakim Jonsson (MIDL, 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; et al. (CoRR, 2021)

MRI bias field correction with an implicitly trained CNN. - Attila Simkó; Tommy Löfstedt; Anders Garpebring; Tufve Nyholm; Joakim Jonsson (MIDL, 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)

Reproducibility of the Methods in Medical Imaging with Deep Learning. - Attila Simkó; Anders Garpebring; Joakim Jonsson; Tufve Nyholm; Tommy Löfstedt (MIDL, 2023)

LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space. - Lorenzo Tronchin; Minh H. Vu; Paolo Soda; Tommy Löfstedt (CoRR, 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., 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, 2023)

A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (Trashfire). - Charles Meyers; Mohammad Reza Saleh Sedghpour; Tommy Löfstedt; Erik Elmroth (ICMLC, 2024)

A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks. - Charles Meyers; Mohammad Reza Saleh Sedghpour; Tommy Löfstedt; Erik Elmroth (CoRR, 2024)

Synthesizing multi-log grasp poses. - Arvid Fälldin; Erik Wallin; Tommy Löfstedt; Martin Servin (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)

A Cost-Aware Approach to Adversarial Robustness in Neural Networks. - Charles Meyers; Mohammad Reza Saleh Sedghpour; Tommy Löfstedt; Erik Elmroth (CoRR, 2024)

Compressing regularised dynamics improves link prediction in sparse networks. - Maja Lindström; Christopher Blöcker; Tommy Löfstedt; Martin Rosvall (CoRR, 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)

Structured Regularization Using Approximate Morphology for Alzheimer's Disease Classification. - Disi Lin; Linus Hägg; Eddie Wadbro; Martin Berggren; Tommy Löfstedt (ISBI, 2025)

From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. - Saeideh Ghanbari Azar; Lorenzo Tronchin; Attila Simkó; Tufve Nyholm; Tommy Löfstedt (Trans. Mach. Learn. Res., 2025)

Using Synthetic Images to Augment Small Medical Image Datasets. - Minh H. Vu; Lorenzo Tronchin; Tufve Nyholm; Tommy Löfstedt (CoRR, 2025)

Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning. - Karlo Palenzuela; Ali Dadras; Alp Yurtsever; Tommy Löfstedt (CoRR, 2025)

Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation. - Lorenzo Tronchin; Tommy Löfstedt; Paolo Soda; Valerio Guarrasi (CoRR, 2025)

Using the Krylov subspace formulation to improve regularisation and interpretation in partial least squares regression. - Tommy Löfstedt (Comput. Stat., 2025)