Zobrazeno 1 - 10
of 304
pro vyhledávání: '"Véra, Pierre"'
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
As information sources are usually imperfect, it is necessary to take into account their reliability in multi-source information fusion tasks. In this paper, we propose a new deep framework allowing us to merge multi-MR image segmentation results usi
Externí odkaz:
http://arxiv.org/abs/2206.11739
Publikováno v:
In Pattern Recognition Letters January 2025 187:93-99
Publikováno v:
Entropy 2022, 24(4), 436
In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis-Havrda-Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in m
Externí odkaz:
http://arxiv.org/abs/2203.11943
Background and Objectives: Predicting patient response to treatment and survival in oncology is a prominent way towards precision medicine. To that end, radiomics was proposed as a field of study where images are used instead of invasive methods. The
Externí odkaz:
http://arxiv.org/abs/2203.00641
Publikováno v:
Neurocomputing 2021
Using multimodal Magnetic Resonance Imaging (MRI) is necessary for accurate brain tumor segmentation. The main problem is that not all types of MRIs are always available in clinical exams. Based on the fact that there is a strong correlation between
Externí odkaz:
http://arxiv.org/abs/2111.04735