Tommy Löfstedt

50 papers B 1Misc 1Journal 34Unranked 12

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches. Trans. Mach. Learn. Res. Minh H. Vu, Daniel Edler, Carl Wibom, Tommy Löfstedt, Beatrice Melin, Martin Rosvall
2026	J	`jnl`	Beyond a single mode: GAN ensembles for diverse medical data generation. Comput. Methods Programs Biomed. Lorenzo Tronchin, Tommy Löfstedt, Paolo Soda, Valerio Guarrasi
2026	J	`jnl`	Tiny, Hardware-Independent, Compression-based Classification. CoRR Charles Meyers, Aaron MacSween, Erik Elmroth, Tommy Löfstedt
2025	—	`conf`	A Proper Structured Prior for Bayesian T UNSURE@MICCAI Disi Lin, Anders Garpebring, Tommy Löfstedt
2025	J	`jnl`	Beyond a Single Mode: GAN Ensembles for Diverse Medical Data Generation. CoRR Lorenzo Tronchin, Tommy Löfstedt, Paolo Soda, Valerio Guarrasi
2025	J	`jnl`	Fairness Regularization in Federated Learning. CoRR Zahra Kharaghani, Ali Dadras, Tommy Löfstedt
2025	J	`jnl`	From Promise to Practice: A Study of Common Pitfalls Behind the Generalization Gap in Machine Learning. Trans. Mach. Learn. Res. Saeideh Ghanbari Azar, Lorenzo Tronchin, Attila Simkó, Tufve Nyholm, Tommy Löfstedt
2025	J	`jnl`	LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space. IEEE Trans. Pattern Anal. Mach. Intell. Lorenzo Tronchin, Minh H. Vu, Paolo Soda, Tommy Löfstedt
2025	—	`conf`	Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning. MLSP Karlo Palenzuela, Ali Dadras, Alp Yurtsever, Tommy Löfstedt
2025	J	`jnl`	Provable Reduction in Communication Rounds for Non-Smooth Convex Federated Learning. CoRR Karlo Palenzuela, Ali Dadras, Alp Yurtsever, Tommy Löfstedt
2025	B	`conf`	Rethinking the DeepSMOTE Penalty Term and its Role in Imbalanced Learning. ICTAI Saeideh Ghanbari Azar, Tufve Nyholm, Tommy Löfstedt
2025	—	`conf`	Structured Regularization Using Approximate Morphology for Alzheimer's Disease Classification. ISBI Disi Lin, Linus Hägg, Eddie Wadbro, Martin Berggren, Tommy Löfstedt
2025	J	`jnl`	Structured regularization with object size selection using mathematical morphology. Pattern Anal. Appl. Disi Lin, Linus Hägg, Eddie Wadbro, Martin Berggren, Tommy Löfstedt
2025	J	`jnl`	Using Synthetic Images to Augment Small Medical Image Datasets. CoRR Minh H. Vu, Lorenzo Tronchin, Tufve Nyholm, Tommy Löfstedt
2025	J	`jnl`	Using the Krylov subspace formulation to improve regularisation and interpretation in partial least squares regression. Comput. Stat. Tommy Löfstedt
2024	J	`jnl`	A Correlation- and Mean-Aware Loss Function and Benchmarking Framework to Improve GAN-based Tabular Data Synthesis. CoRR Minh H. Vu, Daniel Edler, Carl Wibom, Tommy Löfstedt, Beatrice Melin, Martin Rosvall
2024	J	`jnl`	A Cost-Aware Approach to Adversarial Robustness in Neural Networks. CoRR Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth
2024	J	`jnl`	A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks. CoRR Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth
2024	Misc	`conf`	A Training Rate and Survival Heuristic for Inference and Robustness Evaluation (Trashfire). ICMLC Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth
2024	J	`jnl`	Compressing regularised dynamics improves link prediction in sparse networks. CoRR Maja Lindström, Christopher Blöcker, Tommy Löfstedt, Martin Rosvall
2024	J	`jnl`	Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect. Trans. Mach. Learn. Res. Minh H. Vu, Anders Garpebring, Tufve Nyholm, Tommy Löfstedt
2024	J	`jnl`	Synthesizing multi-log grasp poses. CoRR Arvid Fälldin, Erik Wallin, Tommy Löfstedt, Martin Servin
2023	J	`jnl`	Improving MR image quality with a multi-task model, using convolutional losses. BMC Medical Imaging Attila Simkó, Simone Ruiter, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Mikael Bylund, Joakim Jonsson
2023	J	`jnl`	LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space. CoRR Lorenzo Tronchin, Minh H. Vu, Paolo Soda, Tommy Löfstedt
2023	—	`conf`	Reproducibility of the Methods in Medical Imaging with Deep Learning. MIDL Attila Simkó, Anders Garpebring, Joakim Jonsson, Tufve Nyholm, Tommy Löfstedt
2023	J	`jnl`	Safety-critical computer vision: an empirical survey of adversarial evasion attacks and defenses on computer vision systems. Artif. Intell. Rev. Charles Meyers, Tommy Löfstedt, Erik Elmroth
2022	J	`jnl`	A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation. IEEE Trans. Medical Imaging Minh H. Vu, Gabriella Norman, Tufve Nyholm, Tommy Löfstedt
2022	—	`conf`	MRI bias field correction with an implicitly trained CNN. MIDL Attila Simkó, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson
2022	J	`jnl`	Reproducibility of the Methods in Medical Imaging with Deep Learning. CoRR Attila Simkó, Anders Garpebring, Joakim Jonsson, Tufve Nyholm, Tommy Löfstedt
2021	J	`jnl`	A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation. CoRR Minh H. Vu, Gabriella Norman, Tufve Nyholm, Tommy Löfstedt
2021	—	`conf`	Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning. MIDL Attila Tibor Simkó, Tommy Löfstedt, Anders Garpebring, Mikael Bylund, Tufve Nyholm, Joakim Jonsson
2021	J	`jnl`	QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR 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
2020	J	`jnl`	A Question-Centric Model for Visual Question Answering in Medical Imaging. CoRR Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman
2020	J	`jnl`	A Question-Centric Model for Visual Question Answering in Medical Imaging. IEEE Trans. Medical Imaging Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman
2020	J	`jnl`	Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation. CoRR Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
2020	—	`conf`	Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation. BrainLes@MICCAI (1) Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
2019	J	`jnl`	End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation. CoRR Minh H. Vu, Guus Grimbergen, Attila Simkó, Tufve Nyholm, Tommy Löfstedt
2019	—	`conf`	Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain. CLEF (Working Notes) Minh H. Vu, Raphael Sznitman, Tufve Nyholm, Tommy Löfstedt
2019	J	`jnl`	Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation. CoRR Minh H. Vu, Guus Grimbergen, Tufve Nyholm, Tommy Löfstedt
2019	—	`conf`	Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer's Disease. ISBI Nicolas Guigui, Cathy Philippe, Arnaud Gloaguen, Slim Karkar, Vincent Guillemot, Tommy Löfstedt, Vincent Frouin
2019	—	`ed.`	OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019, Proceedings OR/MLCN@MICCAI Luping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel A. Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang
2019	—	`conf`	TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks. BrainLes@MICCAI (1) Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
2019	J	`jnl`	TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks. CoRR Minh H. Vu, Tufve Nyholm, Tommy Löfstedt
2018	J	`jnl`	Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging. IEEE Trans. Medical Imaging Fouad Hadj-Selem, Tommy Löfstedt, Elvis Dohmatob, Vincent Frouin, Mathieu Dubois, Vincent Guillemot, Edouard Duchesnay
2018	—	`conf`	Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity. PRNI Amicie de Pierrefeu, Tommy Löfstedt, Charles Laidi, Fouad Hadj-Selem, Marion Leboyer, Philippe Ciuciu, Josselin Houenou, Edouard Duchesnay
2018	J	`jnl`	Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty. IEEE Trans. Medical Imaging Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Renaud Jardri, Thomas Fovet, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay
2018	—	`ed.`	Understanding and Interpreting Machine Learning in Medical Image Computing Applications - First International Workshops MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings MLCN/DLF/iMIMIC@MICCAI Danail Stoyanov, Zeike Taylor, Seyed Mostafa Kia, Ipek Oguz, Mauricio Reyes, Anne L. Martel, Lena Maier-Hein, Andre F. Marquand, Edouard Duchesnay, Tommy Löfstedt, Bennett A. Landman, M. Jorge Cardoso, Carlos A. Silva, Sérgio Pereira, Raphael Meier
2014	—	`conf`	Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI. PRNI Mathieu Dubois, Fouad Hadj-Selem, Tommy Löfstedt, Matthieu Perrot, Clara Fischer, Vincent Frouin, Edouard Duchesnay
2012	J	`jnl`	Dynamic ultrasound imaging - A multivariate approach for the analysis and comparison of time-dependent musculoskeletal movements. BMC Medical Imaging Tommy Löfstedt, Olof Ahnlund, Michael Peolsson, Johan Trygg
2010	J	`jnl`	Modelling human musculoskeletal functional movements using ultrasound imaging. BMC Medical Imaging Michael Peolsson, Tommy Löfstedt, Susanna Vogt, Hans Stenlund, Anton Arndt, Johan Trygg