过去一年中添加的文章，按日期排序
BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
ON Manzari, JM Kaleybar, H Saadat… - arXiv preprint arXiv …, 2024 - arxiv.org
143 天前 - The accurate segmentation of medical images is critical for various healthcare
applications. Convolutional neural networks (CNNs), especially Fully Convolutional
Networks (FCNs) like U-Net, have shown remarkable success in medical image
segmentation tasks. However, they have limitations in capturing global context and long-
range relations, especially for objects with significant variations in shape, scale, and texture.
While transformers have achieved state-of-the-art results in natural language processing …
applications. Convolutional neural networks (CNNs), especially Fully Convolutional
Networks (FCNs) like U-Net, have shown remarkable success in medical image
segmentation tasks. However, they have limitations in capturing global context and long-
range relations, especially for objects with significant variations in shape, scale, and texture.
While transformers have achieved state-of-the-art results in natural language processing …
BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
O Nejati Manzari, J Mirzapour Kaleybar… - arXiv e …, 2024 - ui.adsabs.harvard.edu
139 天前 - The accurate segmentation of medical images is critical for various healthcare
applications. Convolutional neural networks (CNNs), especially Fully Convolutional
Networks (FCNs) like U-Net, have shown remarkable success in medical image
segmentation tasks. However, they have limitations in capturing global context and long-
range relations, especially for objects with significant variations in shape, scale, and texture.
While transformers have achieved state-of-the-art results in natural language processing …
applications. Convolutional neural networks (CNNs), especially Fully Convolutional
Networks (FCNs) like U-Net, have shown remarkable success in medical image
segmentation tasks. However, they have limitations in capturing global context and long-
range relations, especially for objects with significant variations in shape, scale, and texture.
While transformers have achieved state-of-the-art results in natural language processing …