Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Mert Yergin"'
Autor:
Ahmet Karagoz, Deniz Alis, Mustafa Ege Seker, Gokberk Zeybel, Mert Yergin, Ilkay Oksuz, Ercan Karaarslan
Publikováno v:
Insights into Imaging, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Objective To evaluate the effectiveness of a self-adapting deep network, trained on large-scale bi-parametric MRI data, in detecting clinically significant prostate cancer (csPCa) in external multi-center data from men of diverse demographic
Externí odkaz:
https://doaj.org/article/b825d9c511e44c1e94a03423ff5a23f1
Autor:
Omer Bagcilar, Deniz Alis, Ceren Alis, Mustafa Ege Seker, Mert Yergin, Ahmet Ustundag, Emil Hikmet, Alperen Tezcan, Gokhan Polat, Ahmet Tugrul Akkus, Fatih Alper, Murat Velioglu, Omer Yildiz, Hakan Hatem Selcuk, Ilkay Oksuz, Osman Kizilkilic, Ercan Karaarslan
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-9 (2023)
Abstract The use of deep learning (DL) techniques for automated diagnosis of large vessel occlusion (LVO) and collateral scoring on computed tomography angiography (CTA) is gaining attention. In this study, a state-of-the-art self-configuring object
Externí odkaz:
https://doaj.org/article/1bde78bbbdf84660812bf903593a17c4
Autor:
Deniz Alis, Ceren Alis, Mert Yergin, Cagdas Topel, Ozan Asmakutlu, Omer Bagcilar, Yeseren Deniz Senli, Ahmet Ustundag, Vefa Salt, Sebahat Nacar Dogan, Murat Velioglu, Hakan Hatem Selcuk, Batuhan Kara, Caner Ozer, Ilkay Oksuz, Osman Kizilkilic, Ercan Karaarslan
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022)
Abstract To investigate the performance of a joint convolutional neural networks-recurrent neural networks (CNN-RNN) using an attention mechanism in identifying and classifying intracranial hemorrhage (ICH) on a large multi-center dataset; to test it
Externí odkaz:
https://doaj.org/article/40d9024177f54153a115a4f0937e7097
Autor:
Deniz Alis, Mert Yergin, Ceren Alis, Cagdas Topel, Ozan Asmakutlu, Omer Bagcilar, Yeseren Deniz Senli, Ahmet Ustundag, Vefa Salt, Sebahat Nacar Dogan, Murat Velioglu, Hakan Hatem Selcuk, Batuhan Kara, Ilkay Oksuz, Osman Kizilkilic, Ercan Karaarslan
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Abstract There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospect
Externí odkaz:
https://doaj.org/article/897683b2837b4094b80466289ed7a30d
Publikováno v:
European Radiology. 31:2706-2715
The cardiac cycle might impair the reproducibility of radiomics features of cardiac magnetic resonance (CMR) cine images, yet this issue has not been addressed in the previous research. We aim to evaluate whether radiomics features of CMR cine images
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::23363ad525baea1119326dca786b366a
https://doi.org/10.1007/s00330-020-07370-y
https://doi.org/10.1007/s00330-020-07370-y
Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783031090011
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7d343fd968d35803feab5fad95f4fd7
https://doi.org/10.1007/978-3-031-09002-8_51
https://doi.org/10.1007/978-3-031-09002-8_51
Autor:
Omer Bagcilar, Sebahat Nacar Dogan, Yeseren Deniz Senli, Deniz Alis, Ilkay Oksuz, Osman Kizilkilic, Ahmet Ustundag, Murat Velioglu, Ozan Asmakutlu, Ercan Karaarslan, Mert Yergin, Cagdas Topel, Hakan Hatem Selcuk, Vefa Salt, Ceren Alis, Batuhan Kara
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
Scientific Reports
Scientific Reports
There is little evidence on the applicability of deep learning (DL) in the segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) between magnetic resonance imaging (MRI) scanners of different manufacturers. We retrospectively inc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df243bd78135d14301dd8c3eae491824
https://doi.org/10.1038/s41598-021-91467-x
https://doi.org/10.1038/s41598-021-91467-x
Autor:
Naci Kocer, Cihan Isler, Yeseren Deniz Senli, Omer Bagcilar, Civan Islak, Osman Kizilkilic, Deniz Alis, Mert Yergin
Publikováno v:
Clinical radiology. 75(5)
AIM To explore the value of quantitative texture analysis of conventional magnetic resonance imaging (MRI) sequences using artificial neural networks (ANN) for the differentiation of high-grade gliomas (HGG) and low-grade gliomas (LGG). MATERIALS AND
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31f2327c1b2178233319a2a525cb3fe4
https://pubmed.ncbi.nlm.nih.gov/31973941
https://pubmed.ncbi.nlm.nih.gov/31973941
Autor:
Cihan Isler, Yeseren Deniz Senli, Mert Yergin, Osman Kizilkilic, Naci Kocer, Civan Islak, Omer Bagcilar, Deniz Alis
Publikováno v:
Japanese journal of radiology. 38(2)
To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG). A total of 142 patients with HGG were included in the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::698dc3afe6be1d81bf71bf9036b34bc1
https://pubmed.ncbi.nlm.nih.gov/31741126
https://pubmed.ncbi.nlm.nih.gov/31741126
Publikováno v:
Diagnostic and interventional imaging. 101(3)
Summary Objective To assess the diagnostic value of machine learning-based texture feature analysis of late gadolinium enhancement images on cardiac magnetic resonance imaging (MRI) for assessing the presence of ventricular tachyarrhythmia (VT) in pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08b6850c75ce7d9085f75ba7c0e6c252
https://pubmed.ncbi.nlm.nih.gov/31727603
https://pubmed.ncbi.nlm.nih.gov/31727603