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2020 – today
- 2024
[j9]Minh H. Vu, Anders Garpebring, Tufve Nyholm, Tommy Löfstedt:
Compressing the Activation Maps in Deep Convolutional Neural Networks and Its Regularizing Effect. Trans. Mach. Learn. Res. 2024 (2024)- [i13]Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth:
A Systematic Approach to Robustness Modelling for Deep Convolutional Neural Networks. CoRR abs/2401.13751 (2024) - [i12]Arvid Fälldin, Erik Wallin, Tommy Löfstedt, Martin Servin:
Synthesizing multi-log grasp poses. CoRR abs/2403.11623 (2024) - [i11]Minh H. Vu, Daniel Edler, Carl Wibom, Tommy Löfstedt, Beatrice Melin, Martin Rosvall:
A Correlation- and Mean-Aware Loss Function and Benchmarking Framework to Improve GAN-based Tabular Data Synthesis. CoRR abs/2405.16971 (2024) - [i10]Charles Meyers, Mohammad Reza Saleh Sedghpour, Tommy Löfstedt, Erik Elmroth:
A Cost-Aware Approach to Adversarial Robustness in Neural Networks. CoRR abs/2409.07609 (2024) - 2023
- [j8]Charles Meyers, Tommy Löfstedt, Erik Elmroth:
Safety-critical computer vision: an empirical survey of adversarial evasion attacks and defenses on computer vision systems. Artif. Intell. Rev. 56(S1): 217-251 (2023) - [j7]Attila Simkó, Simone Ruiter, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Mikael Bylund, Joakim Jonsson:
Improving MR image quality with a multi-task model, using convolutional losses. BMC Medical Imaging 23(1): 148 (2023) - [c9]Attila Simkó, Anders Garpebring, Joakim Jonsson, Tufve Nyholm, Tommy Löfstedt:
Reproducibility of the Methods in Medical Imaging with Deep Learning. MIDL 2023: 95-106 - [i9]Lorenzo Tronchin, Minh H. Vu, Paolo Soda, Tommy Löfstedt:
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space. CoRR abs/2307.11375 (2023) - 2022
- [j6]Minh H. Vu
, Gabriella Norman, Tufve Nyholm, Tommy Löfstedt:
A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation. IEEE Trans. Medical Imaging 41(6): 1320-1330 (2022) - [c8]Attila Simkó, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson:
MRI bias field correction with an implicitly trained CNN. MIDL 2022: 1125-1138 - [i8]Attila Simkó, Anders Garpebring, Joakim Jonsson, Tufve Nyholm, Tommy Löfstedt:
Reproducibility of the Methods in Medical Imaging with Deep Learning. CoRR abs/2210.11146 (2022) - 2021
- [c7]Attila Tibor Simkó, Tommy Löfstedt, Anders Garpebring, Mikael Bylund, Tufve Nyholm, Joakim Jonsson:
Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning. MIDL 2021: 713-727 - [i7]Minh H. Vu, Gabriella Norman, Tufve Nyholm, Tommy Löfstedt:
A Data-Adaptive Loss Function for Incomplete Data and Incremental Learning in Semantic Image Segmentation. CoRR abs/2104.11020 (2021) - [i6]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:
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Metrics and Benchmarking Results. CoRR abs/2112.10074 (2021) - 2020
- [j5]Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman:
A Question-Centric Model for Visual Question Answering in Medical Imaging. IEEE Trans. Medical Imaging 39(9): 2856-2868 (2020) - [c6]Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation. BrainLes@MICCAI (1) 2020: 412-423 - [i5]Minh H. Vu, Tommy Löfstedt, Tufve Nyholm, Raphael Sznitman:
A Question-Centric Model for Visual Question Answering in Medical Imaging. CoRR abs/2003.08760 (2020) - [i4]Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation. CoRR abs/2012.03684 (2020)
2010 – 2019
- 2019
- [c5]Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks. BrainLes@MICCAI (1) 2019: 174-186 - [c4]Minh H. Vu, Raphael Sznitman, Tufve Nyholm, Tommy Löfstedt:
Ensemble of Streamlined Bilinear Visual Question Answering Models for the ImageCLEF 2019 Challenge in the Medical Domain. CLEF (Working Notes) 2019 - [c3]Nicolas Guigui, Cathy Philippe, Arnaud Gloaguen, Slim Karkar, Vincent Guillemot, Tommy Löfstedt, Vincent Frouin:
Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer's Disease. ISBI 2019: 1403-1406 - [e2]Luping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel A. Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang:
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. Lecture Notes in Computer Science 11796, Springer 2019, ISBN 978-3-030-32694-4 [contents] - [i3]Minh H. Vu, Tufve Nyholm, Tommy Löfstedt:
TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks. CoRR abs/1910.05338 (2019) - [i2]Minh H. Vu, Guus Grimbergen, Attila Simkó, Tufve Nyholm, Tommy Löfstedt:
End-to-End Cascaded U-Nets with a Localization Network for Kidney Tumor Segmentation. CoRR abs/1910.07521 (2019) - [i1]Minh H. Vu, Guus Grimbergen, Tufve Nyholm, Tommy Löfstedt:
Evaluation of Multi-Slice Inputs to Convolutional Neural Networks for Medical Image Segmentation. CoRR abs/1912.09287 (2019) - 2018
- [j4]Amicie de Pierrefeu, Tommy Löfstedt, Fouad Hadj-Selem, Mathieu Dubois, Renaud Jardri, Thomas Fovet, Philippe Ciuciu, Vincent Frouin, Edouard Duchesnay:
Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty. IEEE Trans. Medical Imaging 37(2): 396-407 (2018) - [j3]Fouad Hadj-Selem, Tommy Löfstedt, Elvis Dohmatob, Vincent Frouin, Mathieu Dubois, Vincent Guillemot, Edouard Duchesnay:
Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging. IEEE Trans. Medical Imaging 37(11): 2403-2413 (2018) - [c2]Amicie de Pierrefeu, Tommy Löfstedt, Charles Laidi, Fouad Hadj-Selem, Marion Leboyer, Philippe Ciuciu, Josselin Houenou, Edouard Duchesnay:
Interpretable and stable prediction of schizophrenia on a large multisite dataset using machine learning with structured sparsity. PRNI 2018: 1-4 - [e1]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:
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. Lecture Notes in Computer Science 11038, Springer 2018, ISBN 978-3-030-02627-1 [contents] - 2014
- [c1]Mathieu Dubois, Fouad Hadj-Selem, Tommy Löfstedt, Matthieu Perrot, Clara Fischer, Vincent Frouin, Edouard Duchesnay:
Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI. PRNI 2014: 1-4 - 2012
- [j2]Tommy Löfstedt, Olof Ahnlund, Michael Peolsson, Johan Trygg:
Dynamic ultrasound imaging - A multivariate approach for the analysis and comparison of time-dependent musculoskeletal movements. BMC Medical Imaging 12: 29 (2012) - 2010
- [j1]Michael Peolsson, Tommy Löfstedt, Susanna Vogt, Hans Stenlund, Anton Arndt, Johan Trygg:
Modelling human musculoskeletal functional movements using ultrasound imaging. BMC Medical Imaging 10: 9 (2010)
Coauthor Index
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last updated on 2024-10-15 00:20 CEST by the dblp team
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