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Large Vision Model (LVM) has recently demonstrated great potential for medical imaging tasks, potentially enabling image enhancement for sparse-view Cone-Beam Computed Tomography (CBCT), despite requiring a substantial amount of data for training. Me
Externí odkaz:
http://arxiv.org/abs/2203.12476
Adaptively Re-weighting Multi-Loss Untrained Transformer for Sparse-View Cone-Beam CT Reconstruction
Cone-Beam Computed Tomography (CBCT) has been proven useful in diagnosis, but how to shorten scanning time with lower radiation dosage and how to efficiently reconstruct 3D image remain as the main issues for clinical practice. The recent development
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbedec70701c73505382e5e8f9c6b44e
http://arxiv.org/abs/2203.12476
http://arxiv.org/abs/2203.12476
Akademický článek
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Publikováno v:
2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR); 2015, p056-060, 5p
Autor:
Xu, Yangdi, Lee, Lok Hin, Drukker, Lior, Yaqub, Mohammad, Papageorghiou, Aris T., Noble, Alison J.
Publikováno v:
Journal of Medical Imaging; Sep/Oct2020, Vol. 7 Issue 5, p57001-57001, 1p
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference [Annu Int Conf IEEE Eng Med Biol Soc] 2023 Jul; Vol. 2023, pp. 1-4.