BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
ON Manzari, JM Kaleybar, H Saadat… - arXiv preprint arXiv …, 2024 - arxiv.org
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 and image recognition, they …
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 and image recognition, they …
BEFUnet: A Hybrid CNN-Transformer Architecture for Precise Medical Image Segmentation
O Nejati Manzari, J Mirzapour Kaleybar… - arXiv e …, 2024 - ui.adsabs.harvard.edu
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 and image recognition, they …
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 and image recognition, they …
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