Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Ertunc Erdil"'
Autor:
Ertunc Erdil, Anton S. Becker, Moritz Schwyzer, Borja Martinez-Tellez, Jonatan R. Ruiz, Thomas Sartoretti, H. Alberto Vargas, A. Irene Burger, Alin Chirindel, Damian Wild, Nicola Zamboni, Bart Deplancke, Vincent Gardeux, Claudia Irene Maushart, Matthias Johannes Betz, Christian Wolfrum, Ender Konukoglu
Publikováno v:
Nature Communications, Vol 15, Iss 1, Pp 1-14 (2024)
Abstract The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can b
Externí odkaz:
https://doaj.org/article/c805cb8195b94f0d8b4205323cae48a9
Publikováno v:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis ISBN: 9783030877347
UNSURE/PIPPI@MICCAI
UNSURE/PIPPI@MICCAI
In the recent years, researchers proposed a number of successful methods to perform out-of-distribution (OOD) detection in deep neural networks (DNNs). So far the scope of the highly accurate methods has been limited to image level classification tas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f47abd0d67e93dd6b4fa57f7122fb567
https://doi.org/10.1007/978-3-030-87735-4_9
https://doi.org/10.1007/978-3-030-87735-4_9
Publikováno v:
Medical Image Analysis, 87
Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise. Semi/self-supervised learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fe21085546156a3e1fc6ce227e0948c
Autor:
Muhammad Usman Ghani, Anna Felicity Hobbiss, Devrim Unay, Mujdat Cetin, Inbal Israely, Theofanis Karayannis, Ertunc Erdil, Ali Ozgur Argunsah, Yazmín Ramiro Cortés
Live fluorescence imaging has shown the dynamic nature of dendritic spines, with changes in shape occurring both during development and in response to activity. The structure of a dendritic spine positively correlates with its functional efficacy. Le
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::458ea8af253cdb620bf96b1483eee459
https://doi.org/10.1101/2020.09.12.294546
https://doi.org/10.1101/2020.09.12.294546
Publikováno v:
Medical Image Analysis, 68
Convolutional Neural Networks (CNNs) work very well for supervised learning problems when the training dataset is representative of the variations expected to be encountered at test time. In medical image segmentation, this premise is violated when t
Autor:
Anna Volokitin, Kerem Can Tezcan, Xiaoran Chen, Ender Konukoglu, Neerav Karani, Ertunc Erdil, Luc Van Gool
Publikováno v:
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 ISBN: 9783030597276
MICCAI (7)
Lecture Notes in Computer Science, 12267
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
MICCAI (7)
Lecture Notes in Computer Science, 12267
Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Probabilistic modelling has been an essential tool in medical image analysis, especially for analyzing brain Magnetic Resonance Images (MRI). Recent deep learning techniques for estimating high-dimensional distributions, in particular Variational Aut
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f9d8c25b6a2e0f749f007c2c0de49498
Publikováno v:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis ISBN: 9783030603649
UNSURE/GRAIL@MICCAI
UNSURE/GRAIL@MICCAI
Quantifying segmentation uncertainty has become an important issue in medical image analysis due to the inherent ambiguity of anatomical structures and its pathologies. Recently, neural network-based uncertainty quantification methods have been succe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c2973c5a75fc38e8579e6214d1c3f6f3
https://doi.org/10.1007/978-3-030-60365-6_2
https://doi.org/10.1007/978-3-030-60365-6_2
Publikováno v:
IEEE Transactions on Medical Imaging. 37:293-305
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. For instance, most active shape and appearance models require landmark points and assume unimodal shape an
Segmenting images of low quality or with missing data is a challenging problem. In such scenarios, exploiting statistical prior information about the shapes to be segmented can improve the segmentation results significantly. Incorporating prior densi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0682f30c2c02d6bd6f7ace3712c8c152
https://aperta.ulakbim.gov.tr/record/68535
https://aperta.ulakbim.gov.tr/record/68535
Publikováno v:
ISBI
Data driven segmentation is an important initial step of shape prior-based segmentation methods since it is assumed that the data term brings a curve to a plausible level so that shape and data terms can then work together to produce better segmentat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64f9c39b97d6b3ee711a64ec7a65838c
https://aperta.ulakbim.gov.tr/record/70087
https://aperta.ulakbim.gov.tr/record/70087