Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Tommy Löfstedt"'
Autor:
Attila Simkó, Mikael Bylund, Gustav Jönsson, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Joakim Jonsson
Publikováno v:
Zeitschrift für Medizinische Physik, Vol 34, Iss 2, Pp 270-277 (2024)
The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily
Externí odkaz:
https://doaj.org/article/f8b8eba5218f4a42988397a4996bad01
Autor:
Attila Simkó, Simone Ruiter, Tommy Löfstedt, Anders Garpebring, Tufve Nyholm, Mikael Bylund, Joakim Jonsson
Publikováno v:
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-14 (2023)
Abstract Purpose During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correcti
Externí odkaz:
https://doaj.org/article/0c047134c4ed442fa550431ceecaf445
Publikováno v:
eLife, Vol 12 (2023)
Recent developments in deep learning, coupled with an increasing number of sequenced proteins, have led to a breakthrough in life science applications, in particular in protein property prediction. There is hope that deep learning can close the gap b
Externí odkaz:
https://doaj.org/article/b4247922f92d474bae24a43d4a1e2c23
Publikováno v:
Journal of Statistical Software, Vol 87, Iss 1, Pp 1-33 (2018)
A currently very active field of research is how to incorporate structure and prior knowledge in machine learning methods. It has lead to numerous developments in the field of non-smooth convex minimization. With recently developed methods it is poss
Externí odkaz:
https://doaj.org/article/384836713f404b7588f49410a45cf05b
Autor:
Vincent Guillemot, Derek Beaton, Arnaud Gloaguen, Tommy Löfstedt, Brian Levine, Nicolas Raymond, Arthur Tenenhaus, Hervé Abdi
Publikováno v:
PLoS ONE, Vol 14, Iss 3, p e0211463 (2019)
We propose a new sparsification method for the singular value decomposition-called the constrained singular value decomposition (CSVD)-that can incorporate multiple constraints such as sparsification and orthogonality for the left and right singular
Externí odkaz:
https://doaj.org/article/6d1b91ef249d4471846cb1c1f8cfa1d2
Publikováno v:
PLoS ONE, Vol 14, Iss 2, p e0212110 (2019)
Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralic
Externí odkaz:
https://doaj.org/article/4c7d551a8c8141a5836708c37b37201e
Deep learning has dramatically improved performance in various image analysis applications in the last few years. However, recent deep learning architectures can be very large, with up to hundreds of layers and millions or even billions of model para
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::785f070486e7412d570f4464aa374d8e
https://doi.org/10.36227/techrxiv.21915921.v1
https://doi.org/10.36227/techrxiv.21915921.v1
Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030720834
BrainLes@MICCAI (1)
BrainLes@MICCAI (1)
Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice. Unfortunately, manual segmentation is very challenging, time-consuming, costly, and often inaccurate despite human expertise due to the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e38fb5393f9640c138ac8dea5db85bc2
https://doi.org/10.1007/978-3-030-72084-1_37
https://doi.org/10.1007/978-3-030-72084-1_37
Autor:
G. Heilemann, L. Fetty, Peter Kuess, Dietmar Georg, Tommy Löfstedt, Tufve Nyholm, Hugo Furtado, Nicole Nesvacil
Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1f2814fab4bc1845ed3c8820a9bef152
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172540
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172540
Autor:
G. Heilemann, Tommy Löfstedt, Mikael Bylund, Dietmar Georg, Peter Kuess, Tufve Nyholm, L. Fetty
Introduction This paper explores the potential of the StyleGAN model as an high-resolution image generator for synthetic medical images. The possibility to generate sample patient images of different modalities can be helpful for training deep learni
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42dabda850af64a42ddb8b1e2d181334
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-177508
http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-177508