Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Phuong Hien Tran"'
Autor:
Marco Reisert, Fabian Bamberg, Maximilian Frederik Russe, Philipp Rebmann, Phuong Hien Tran, Elias Kellner, Elmar Kotter, Suam Kim
Publikováno v:
BMJ Open, Vol 14, Iss 1 (2024)
Objectives To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models for
Externí odkaz:
https://doaj.org/article/834af8f50dd046dc81f29debdab02356
Autor:
Suam Kim, Philipp Rebmann, Phuong Hien Tran, Elias Kellner, Marco Reisert, David Steybe, Jörg Bayer, Fabian Bamberg, Elmar Kotter, Maximilian Russe
Publikováno v:
International Journal of Computer Assisted Radiology and Surgery. 18:819-826
Purpose Artificial intelligence in computer vision has been increasingly adapted in clinical application since the implementation of neural networks, potentially providing incremental information beyond the mere detection of pathology. As its algorit
Autor:
Trung Thanh Le, Cong Doanh Duong, Xuan Hau Doan, Thi Phuong Linh Nguyen, Thi Phuong Hien Tran, Duc Dung Tran
Publikováno v:
Asia-Pacific Journal of Business Administration. 13:497-519
PurposeThis study aims to integrate predictions from clinical psychology and UPPS impulsivity with the theory of planned behaviors (TPB) to draw a conceptual framework and test the prediction that attention deficit hyperactivity disorder (ADHD) sympt
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
David Steybe, Philipp Poxleitner, Marc Christian Metzger, Leonard Simon Brandenburg, Rainer Schmelzeisen, Fabian Bamberg, Phuong Hien Tran, Elias Kellner, Marco Reisert, Maximilian Frederik Russe
Publikováno v:
International journal of computer assisted radiology and surgery. 17(11)
Purpose Computer-assisted techniques play an important role in craniomaxillofacial surgery. As segmentation of three-dimensional medical imaging represents a cornerstone for these procedures, the present study was aiming at investigating a deep learn