Abstract
For the past few years, the U-Net structure shows strong performance in the field of medical image segmentation. However, due to the inherent locality of convolution operations, U-shaped structures are often limited in modeling long-range dependencies. Transformer, a global self-attention mechanism designed for sequence-to-sequence prediction, has been successfully used in the field of computer vision. In this paper, we propose a novel network, named TransHarDNet. HarDNet, which is a low memory traffic CNN. We combine it as backbone with Transformer. Our network enables the global semantic context information and low-level spatial details of the input image to be captured more effectively. We evaluate the effectiveness of the proposed network on five medical image datasets.
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Wang, Q. et al. (2022). Medical Image Segmentation Using Transformer. In: Liang, Q., Wang, W., Mu, J., Liu, X., Na, Z. (eds) Artificial Intelligence in China. Lecture Notes in Electrical Engineering, vol 854. Springer, Singapore. https://doi.org/10.1007/978-981-16-9423-3_12
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DOI: https://doi.org/10.1007/978-981-16-9423-3_12
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