Zobrazeno 1 - 10
of 415
pro vyhledávání: '"RUAN Su"'
Publikováno v:
康复学报, Vol 32, Pp 32-39 (2022)
ObjectiveThe purpose of this study is to explore the mechanism of electroacupuncture at Baihui and Shenting acupoints to alleviate cerebral ischemia-reperfusion injury by regulating BNIP3L-mediated mitophagy.MethodsA total of 60 SD rats were randomly
Externí odkaz:
https://doaj.org/article/a4e0ef8e68484884849f730cca6b382c
Multimodal survival analysis aims to combine heterogeneous data sources (e.g., clinical, imaging, text, genomics) to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noi
Externí odkaz:
http://arxiv.org/abs/2412.01215
Autor:
Mesbah, Zacharia, Mottay, Léo, Modzelewski, Romain, Decazes, Pierre, Hapdey, Sébastien, Ruan, Su, Thureau, Sébastien
For the last three years, the AutoPET competition gathered the medical imaging community around a hot topic: lesion segmentation on Positron Emitting Tomography (PET) scans. Each year a different aspect of the problem is presented; in 2024 the multip
Externí odkaz:
http://arxiv.org/abs/2410.02807
In this paper, we propose a self-prior guided Mamba-UNet network (SMamba-UNet) for medical image super-resolution. Existing methods are primarily based on convolutional neural networks (CNNs) or Transformers. CNNs-based methods fail to capture long-r
Externí odkaz:
http://arxiv.org/abs/2407.05993
In this paper, we propose a new architecture, called Deform-Mamba, for MR image super-resolution. Unlike conventional CNN or Transformer-based super-resolution approaches which encounter challenges related to the local respective field or heavy compu
Externí odkaz:
http://arxiv.org/abs/2407.05969
Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurate results. In efforts to overcome this challenge, data augmentation te
Externí odkaz:
http://arxiv.org/abs/2406.11659
Despite the increasing use of deep learning in medical image segmentation, the limited availability of annotated training data remains a major challenge due to the time-consuming data acquisition and privacy regulations. In the context of segmentatio
Externí odkaz:
http://arxiv.org/abs/2406.05421
Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentation techniques have been proposed; however, the generation of diverse a
Externí odkaz:
http://arxiv.org/abs/2311.10472
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adop
Externí odkaz:
http://arxiv.org/abs/2310.06873
Single-modality medical images generally do not contain enough information to reach an accurate and reliable diagnosis. For this reason, physicians generally diagnose diseases based on multimodal medical images such as, e.g., PET/CT. The effective fu
Externí odkaz:
http://arxiv.org/abs/2309.05919