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pro vyhledávání: '"Wittmann, Bastian"'
Autor:
Hamamci, Ibrahim Ethem, Er, Sezgin, Almas, Furkan, Simsek, Ayse Gulnihan, Esirgun, Sevval Nil, Dogan, Irem, Dasdelen, Muhammed Furkan, Wittmann, Bastian, Simsar, Enis, Simsar, Mehmet, Erdemir, Emine Bensu, Alanbay, Abdullah, Sekuboyina, Anjany, Lafci, Berkan, Ozdemir, Mehmet K., Menze, Bjoern
A major challenge in computational research in 3D medical imaging is the lack of comprehensive datasets. Addressing this issue, our study introduces CT-RATE, the first 3D medical imaging dataset that pairs images with textual reports. CT-RATE consist
Externí odkaz:
http://arxiv.org/abs/2403.17834
Autor:
Wittmann, Bastian, Glandorf, Lukas, Paetzold, Johannes C., Amiranashvili, Tamaz, Wälchli, Thomas, Razansky, Daniel, Menze, Bjoern
Segmentation of blood vessels in murine cerebral 3D OCTA images is foundational for in vivo quantitative analysis of the effects of neurovascular disorders, such as stroke or Alzheimer's, on the vascular network. However, to accurately segment blood
Externí odkaz:
http://arxiv.org/abs/2403.07116
Autor:
Wittmann, Bastian, Paetzold, Johannes C., Prabhakar, Chinmay, Rueckert, Daniel, Menze, Bjoern
Link prediction algorithms aim to infer the existence of connections (or links) between nodes in network-structured data and are typically applied to refine the connectivity among nodes. In this work, we focus on link prediction for flow-driven spati
Externí odkaz:
http://arxiv.org/abs/2303.14501
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2023)
Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation modeling. Although Detection Transformers deliver results on par with
Externí odkaz:
http://arxiv.org/abs/2207.10774
Autor:
Shit, Suprosanna, Koner, Rajat, Wittmann, Bastian, Paetzold, Johannes, Ezhov, Ivan, Li, Hongwei, Pan, Jiazhen, Sharifzadeh, Sahand, Kaissis, Georgios, Tresp, Volker, Menze, Bjoern
A comprehensive representation of an image requires understanding objects and their mutual relationship, especially in image-to-graph generation, e.g., road network extraction, blood-vessel network extraction, or scene graph generation. Traditionally
Externí odkaz:
http://arxiv.org/abs/2203.10202
Publikováno v:
Machine Learning for Biomedical Imaging. 2:72-95
Detection Transformers represent end-to-end object detection approaches based on a Transformer encoder-decoder architecture, exploiting the attention mechanism for global relation modeling. Although Detection Transformers deliver results on par with
Autor:
Wittmann, Bastian
submitted by Bastian Wittmann Abweichender Titel laut Übersetzung der Verfasserin/des Verfassers Arbeit gesperrt bis Universität Linz, Masterarbeit, 2018
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3361::6531b0fe669a54230b2be37c91adf324