Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Ozan Oktay"'
Autor:
Kenza Bouzid, Harshita Sharma, Sarah Killcoyne, Daniel C. Castro, Anton Schwaighofer, Max Ilse, Valentina Salvatelli, Ozan Oktay, Sumanth Murthy, Lucas Bordeaux, Luiza Moore, Maria O’Donovan, Anja Thieme, Aditya Nori, Marcel Gehrung, Javier Alvarez-Valle
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-15 (2024)
Abstract Timely detection of Barrett’s esophagus, the pre-malignant condition of esophageal adenocarcinoma, can improve patient survival rates. The Cytosponge-TFF3 test, a non-endoscopic minimally invasive procedure, has been used for diagnosing in
Externí odkaz:
https://doaj.org/article/a7d26ad27adf4210ba573b93bfdee22b
Autor:
Mélanie Bernhardt, Daniel C. Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C. Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew P. Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-11 (2022)
High quality labels are important for model performance, evaluation and selection in medical imaging. As manual labelling is time-consuming and costly, the authors explore and benchmark various resource-effective methods for improving dataset quality
Externí odkaz:
https://doaj.org/article/70d06a2166804fea9694afa4ba328c23
Autor:
Robert Robinson, Vanya V. Valindria, Wenjia Bai, Ozan Oktay, Bernhard Kainz, Hideaki Suzuki, Mihir M. Sanghvi, Nay Aung, José Miguel Paiva, Filip Zemrak, Kenneth Fung, Elena Lukaschuk, Aaron M. Lee, Valentina Carapella, Young Jin Kim, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Chris Page, Paul M. Matthews, Daniel Rueckert, Ben Glocker
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 21, Iss 1, Pp 1-14 (2019)
Abstract Background The trend towards large-scale studies including population imaging poses new challenges in terms of quality control (QC). This is a particular issue when automatic processing tools such as image segmentation methods are employed t
Externí odkaz:
https://doaj.org/article/e9f719e43c4e4bf594589361294c73e0
Autor:
Wenjia Bai, Matthew Sinclair, Giacomo Tarroni, Ozan Oktay, Martin Rajchl, Ghislain Vaillant, Aaron M. Lee, Nay Aung, Elena Lukaschuk, Mihir M. Sanghvi, Filip Zemrak, Kenneth Fung, Jose Miguel Paiva, Valentina Carapella, Young Jin Kim, Hideaki Suzuki, Bernhard Kainz, Paul M. Matthews, Steffen E. Petersen, Stefan K. Piechnik, Stefan Neubauer, Ben Glocker, Daniel Rueckert
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 20, Iss 1, Pp 1-12 (2018)
Abstract Background Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection f
Externí odkaz:
https://doaj.org/article/d747f65dcc3043c6921822cc293970e3
Autor:
Alberto Gomez, Ozan Oktay, Daniel Rueckert, Graeme Patrick Penney, Julia Anne Schnabel, John M Simpson, Kuberan Pushparajah
Publikováno v:
Frontiers in Pediatrics, Vol 4 (2016)
Ultrasound is commonly thought to underestimate ventricular volumes compared to Magnetic Resonance Imaging (MRI), although the reason for this and the spatial distribution of the volume difference is not well understood. In this paper, we use landmar
Externí odkaz:
https://doaj.org/article/a05cd896d9864143b7b9c36509859e57
Autor:
Stuart A. Cook, Sanjay Prasad, Konstantinos Kamnitsas, Giacomo Tarroni, Ozan Oktay, Daniel Rueckert, Jinming Duan, Christian Ledig, Georgia Doumou, Loic Le Folgoc, Carlo Biffi, Declan P. O'Regan, Wenjia Bai, Juan J. Cerrolaza, Antonio de Marvao
Publikováno v:
IEEE Trans Med Imaging
Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagno
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::02973443d271b95838de124c07cdf769
https://doi.org/10.1109/tmi.2020.2964499
https://doi.org/10.1109/tmi.2020.2964499
Autor:
Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, Ozan Oktay
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031200588
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::907f669f41e07274f962a309a9855503
https://doi.org/10.1007/978-3-031-20059-5_1
https://doi.org/10.1007/978-3-031-20059-5_1
Autor:
Mélanie Bernhardt, Daniel C. Castro, Ryutaro Tanno, Anton Schwaighofer, Kerem C. Tezcan, Miguel Monteiro, Shruthi Bannur, Matthew P. Lungren, Aditya Nori, Ben Glocker, Javier Alvarez-Valle, Ozan Oktay
Publikováno v:
Nature communications. 13(1)
Imperfections in data annotation, known as label noise, are detrimental to the training of machine learning models and have an often-overlooked confounding effect on the assessment of model performance. Nevertheless, employing experts to remove label
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::624c49f1d089b6bcc56908fd48450a9d
https://pubmed.ncbi.nlm.nih.gov/35246539
https://pubmed.ncbi.nlm.nih.gov/35246539
Autor:
Amir Alansary, Athanasios Vlontzos, Konstantinos Kamnitsas, Yuanwei Li, Ozan Oktay, Benjamin Hou, Bernhard Kainz, Loic Le Folgoc, Daniel Rueckert, Ben Glocker, Ghislain Vaillant
Publikováno v:
Med Image Anal
Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16c549303201024dfb8f93911e06a07c
https://doi.org/10.1016/j.media.2019.02.007
https://doi.org/10.1016/j.media.2019.02.007
Autor:
Paul M. Matthews, Ozan Oktay, Hideaki Suzuki, Giacomo Tarroni, Daniel Rueckert, Ben Glocker, Andreas Schuh, Wenjia Bai
Publikováno v:
Scientific Reports
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
Scientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::640156a4d3b120df4666c7f54e6d2fa2
https://openaccess.city.ac.uk/id/eprint/23836/1/s41598-020-58212-2.pdf
https://openaccess.city.ac.uk/id/eprint/23836/1/s41598-020-58212-2.pdf