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pro vyhledávání: '"Pálsson, Sveinn"'
In this paper we propose a method for predicting the status of MGMT promoter methylation in high-grade gliomas. From the available MR images, we segment the tumor using deep convolutional neural networks and extract both radiomic features and shape f
Externí odkaz:
http://arxiv.org/abs/2109.12339
Autor:
Pálsson, Sveinn, Cerri, Stefano, Poulsen, Hans Skovgaard, Urup, Thomas, Law, Ian, Van Leemput, Koen
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing acros
Externí odkaz:
http://arxiv.org/abs/2109.12334
In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning
Externí odkaz:
http://arxiv.org/abs/1910.04488
Akademický článek
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Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783031090011
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d43e4fa472f969c96c2c4482651b124b
https://doi.org/10.1007/978-3-031-09002-8_20
https://doi.org/10.1007/978-3-031-09002-8_20
Autor:
Pálsson, Sveinn
Publikováno v:
Pálsson, S 2021, Robust Imaging Biomarkers for Brain Tumors . DTU Health Technology .
The goal of this PhD project is to develop robust imaging biomarkers for buildingprediction models of brain tumors. Brain tumor biomarkers are used for analysis of the disease, e.g. with respect to tumor grade, tumor recurrence and survival. We focus
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1202::e49c0ab79ed98f18f5570258041223d2
https://orbit.dtu.dk/en/publications/4ff63a6f-3300-4789-ae85-4797ab0dd27c
https://orbit.dtu.dk/en/publications/4ff63a6f-3300-4789-ae85-4797ab0dd27c
Publikováno v:
Pálsson, S, Cerri, S, Dittadi, A & Leemput, K V 2020, Semi-supervised variational autoencoder for survival prediction . in A Crimi & S Bakas (eds), Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries . Springer, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11993 LNCS, pp. 124-134, 5th International MICCAI Brainlesion Workshop, held in conjunction with the Medical Image Computing for Computer Assisted Intervention, Shenzhen, China, 17/10/2019 . https://doi.org/10.1007/978-3-030-46643-5_12
In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______1202::7dd6f847197194424404bc8674f4810a
https://orbit.dtu.dk/en/publications/51b23f33-01a0-4c7e-8c48-40dfbb838cc8
https://orbit.dtu.dk/en/publications/51b23f33-01a0-4c7e-8c48-40dfbb838cc8