Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Wagner, Matthias W"'
Autor:
Ketabi, Sara, Wagner, Matthias W., Hawkins, Cynthia, Tabori, Uri, Ertl-Wagner, Birgit Betina, Khalvati, Farzad
Despite the promising performance of convolutional neural networks (CNNs) in brain tumor diagnosis from magnetic resonance imaging (MRI), their integration into the clinical workflow has been limited. That is mainly due to the fact that the features
Externí odkaz:
http://arxiv.org/abs/2411.00609
Autor:
Namdar, Khashayar, Wagner, Matthias W., Hawkins, Cynthia, Tabori, Uri, Ertl-Wagner, Birgit B., Khalvati, Farzad
Pediatric Low-Grade Neuroepithelial Tumors (PLGNT) are the most common pediatric cancer type, accounting for 40% of brain tumors in children, and identifying PLGNT molecular subtype is crucial for treatment planning. However, the gold standard to det
Externí odkaz:
http://arxiv.org/abs/2402.03547
Autor:
Zhou, Meng, Wagner, Matthias W, Tabori, Uri, Hawkins, Cynthia, Ertl-Wagner, Birgit B, Khalvati, Farzad
Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. However,
Externí odkaz:
http://arxiv.org/abs/2310.01251
Autor:
Yoo, Jay J., Namdar, Khashayar, Wagner, Matthias W., Nobre, Liana, Tabori, Uri, Hawkins, Cynthia, Ertl-Wagner, Birgit B., Khalvati, Farzad
Segmentation of regions of interest (ROIs) for identifying abnormalities is a leading problem in medical imaging. Using machine learning for this problem generally requires manually annotated ground-truth segmentations, demanding extensive time and r
Externí odkaz:
http://arxiv.org/abs/2211.05269
Autor:
Namdar, Khashayar, Wagner, Matthias W., Kudus, Kareem, Hawkins, Cynthia, Tabori, Uri, Ertl-Wagner, Brigit, Khalvati, Farzad
Background and Purpose: Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, and identification of molecular markers for pLGG is crucial for successful treatment planning. Convolutional Neural Network (CNN) models for
Externí odkaz:
http://arxiv.org/abs/2210.07287
Purpose: As an important branch of machine learning pipelines in medical imaging, radiomics faces two major challenges namely reproducibility and accessibility. In this work, we introduce open-radiomics, a set of radiomics datasets along with a compr
Externí odkaz:
http://arxiv.org/abs/2207.14776
Autor:
Kudus, Kareem1,2, Wagner, Matthias W.3,4, Namdar, Khashayar1,2, Bennett, Julie5,6,7, Nobre, Liana8,9, Tabori, Uri5, Hawkins, Cynthia10, Ertl-Wagner, Birgit Betina1,2,3,11, Khalvati, Farzad1,2,3,11,12,13 farzad.khalvati@utoronto.ca
Publikováno v:
Scientific Reports. 8/17/2024, Vol. 14 Issue 1, p1-9. 9p.
Autor:
Vafaeikia, Partoo, Wagner, Matthias W., Tabori, Uri, Ertl-Wagner, Birgit B., Khalvati, Farzad
Brain tumor segmentation is a critical task for tumor volumetric analyses and AI algorithms. However, it is a time-consuming process and requires neuroradiology expertise. While there has been extensive research focused on optimizing brain tumor segm
Externí odkaz:
http://arxiv.org/abs/2111.14959
Publikováno v:
In Biomedical Signal Processing and Control March 2023 81
Publikováno v:
Child's Nervous System; Oct2024, Vol. 40 Issue 10, p3003-3006, 4p