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pro vyhledávání: '"Karanam, Mokshagna Sai Teja"'
Transformers have emerged as the state-of-the-art architecture in medical image registration, outperforming convolutional neural networks (CNNs) by addressing their limited receptive fields and overcoming gradient instability in deeper models. Despit
Externí odkaz:
http://arxiv.org/abs/2403.11026
Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape models use consistent shape representation across all the samples in a give
Externí odkaz:
http://arxiv.org/abs/2307.03273
Autor:
Karanam MST; Kahlert School of Computing, University Of Utah.; Scientific Computing and Imaging Institute, University of Utah., Kataria T; Kahlert School of Computing, University Of Utah.; Scientific Computing and Imaging Institute, University of Utah., Iyer K; Kahlert School of Computing, University Of Utah.; Scientific Computing and Imaging Institute, University of Utah., Elhabian SY; Kahlert School of Computing, University Of Utah.; Scientific Computing and Imaging Institute, University of Utah.
Publikováno v:
Shape in medical imaging : International Workshop, ShapeMI 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ShapeMI (Workshop) (2023 : Vancouver, B.C.) [Shape Med Imaging (2023)] 2023 Oct; Vol. 14350, pp. 90-104. Date of Electronic Publication: 2023 Oct 31.