Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Mengwei Ren"'
Publikováno v:
Frontiers in Oncology, Vol 12 (2023)
BackgroundTRIM37 has been reported to be associated with the tumorigenesis of cancers. However, the role of TRIM37 in T-cell acute lymphoblastic leukemia (T-ALL) remains unclear. This study aimed to characterize the effect of TRIM37 on T-ALL.MethodsT
Externí odkaz:
https://doaj.org/article/5a29d6e8b2834345b286e7216dd82e85
Autor:
Mengwei Ren, Yanli Liu
Publikováno v:
Journal of Medical Case Reports, Vol 15, Iss 1, Pp 1-5 (2021)
Abstract Background Primary lymphoma of the prostate is an exceedingly rare disease, with diffuse large B-cell lymphoma being the most common known subtype in a small number of reported cases. Due to its low prevalence, there has been a chronic lack
Externí odkaz:
https://doaj.org/article/626caff1107c4823897d6ba757f3e2ab
Autor:
Yanli Liu, Mengwei Ren
Publikováno v:
Journal of Medical Case Reports, Vol 15, Iss 1, Pp 1-5 (2021)
Journal of Medical Case Reports
Journal of Medical Case Reports
Background Primary lymphoma of the prostate is an exceedingly rare disease, with diffuse large B-cell lymphoma being the most common known subtype in a small number of reported cases. Due to its low prevalence, there has been a chronic lack of target
Publikováno v:
IEEE Trans Med Imaging
Deep networks are now ubiquitous in large-scale multi-center imaging studies. However, the direct aggregation of images across sites is contraindicated for downstream statistical and deep learning-based image analysis due to inconsistent contrast, re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9faea9fa81442dc06c632377daadaf3d
https://europepmc.org/articles/PMC8294062/
https://europepmc.org/articles/PMC8294062/
Publikováno v:
Med Image Comput Comput Assist Interv
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872335
MICCAI (7)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 ISBN: 9783030872335
MICCAI (7)
Current deep learning approaches for diffusion MRI modeling circumvent the need for densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs. However, they implicitly make unrealistic
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ec7011018596f392ebe7a27144e5c81d