Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Braren, Rickmer"'
Autor:
Ziller, Alexander, Güvenir, Alp, Erdur, Ayhan Can, Mueller, Tamara T., Müller, Philip, Jungmann, Friederike, Brandt, Johannes, Peeken, Jan, Braren, Rickmer, Rueckert, Daniel, Kaissis, Georgios
Training Artificial Intelligence (AI) models on three-dimensional image data presents unique challenges compared to the two-dimensional case: Firstly, the computational resources are significantly higher, and secondly, the availability of large pretr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2a052fd7dcacb58b2a2b2cda9e452007
Autor:
Czempiel, Tobias, Rogers, Coco, Keicher, Matthias, Paschali, Magdalini, Braren, Rickmer, Burian, Egon, Makowski, Marcus, Navab, Nassir, Wendler, Thomas, Kim, Seong Tae
Quantifying COVID-19 infection over time is an important task to manage the hospitalization of patients during a global pandemic. Recently, deep learning-based approaches have been proposed to help radiologists automatically quantify COVID-19 patholo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b210ed139444bffacf053f1ce1b17e5
Autor:
Liebl, Hans, Schinz, David, Sekuboyina, Anjany, Malagutti, Luca, Löffler, Maximilian T., Bayat, Amirhossein, El Husseini, Malek, Tetteh, Giles, Grau, Katharina, Niederreiter, Eva, Baum, Thomas, Wiestler, Benedikt, Menze, Bjoern, Braren, Rickmer, Zimmer, Claus, Kirschke, Jan S.
Publikováno v:
Scientific Data, Vol 8, Iss 1, Pp 1-7 (2021)
Scientific Data
Scientific Data
With the advent of deep learning algorithms, fully automated radiological image analysis is within reach. In spine imaging, several atlas- and shape-based as well as deep learning segmentation algorithms have been proposed, allowing for subsequent au
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8969dd8c97aaccccbe85bbfb1f2dfe43
Autor:
Foo, Michelle Xiao-Lin, Kim, Seong Tae, Paschali, Magdalini, Goli, Leili, Burian, Egon, Makowski, Marcus, Braren, Rickmer, Navab, Nassir, Wendler, Thomas
Consistent segmentation of COVID-19 patient's CT scans across multiple time points is essential to assess disease progression and response to therapy accurately. Existing automatic and interactive segmentation models for medical images only use data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9b8822e120e36dfccc3410df93c79c89
Autor:
Kim, Seong Tae, Goli, Leili, Paschali, Magdalini, Khakzar, Ashkan, Keicher, Matthias, Czempiel, Tobias, Burian, Egon, Braren, Rickmer, Navab, Nassir, Wendler, Thomas
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation. Image segmentation methods have proven to help quant
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d741ecd8298a003bca3bac90f4f296b6
Autor:
Ziller, Alexander, Usynin, Dmitrii, Remerscheid, Nicolas, Knolle, Moritz, Makowski, Marcus, Braren, Rickmer, Rueckert, Daniel, Kaissis, Georgios
Collaborative machine learning techniques such as federated learning (FL) enable the training of models on effectively larger datasets without data transfer. Recent initiatives have demonstrated that segmentation models trained with FL can achieve pe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fc1f5e72096790ef076b5a914ca41142