HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation

• URL: http://arxiv.org/abs/2207.08518v1
• Date: Mon, 18 Jul 2022 11:30:06 GMT
• Title: HiFormer: Hierarchical Multi-scale Representations Using Transformers for Medical Image Segmentation
• Authors: Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad, Ehsan Khodapanah Aghdam, Julien Cohen-Adad, Dorit Merhof
• Abstract summary: HiFormer is a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. To secure a fine fusion of global and local features, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure.
• Score: 3.478921293603811
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contrast, it is demonstrated that both local and global features are crucial for dense prediction, such as segmenting in challenging contexts. In this paper, we propose HiFormer, a novel method that efficiently bridges a CNN and a transformer for medical image segmentation. Specifically, we design two multi-scale feature representations using the seminal Swin Transformer module and a CNN-based encoder. To secure a fine fusion of global and local features obtained from the two aforementioned representations, we propose a Double-Level Fusion (DLF) module in the skip connection of the encoder-decoder structure. Extensive experiments on various medical image segmentation datasets demonstrate the effectiveness of HiFormer over other CNN-based, transformer-based, and hybrid methods in terms of computational complexity, and quantitative and qualitative results. Our code is publicly available at: https://github.com/amirhossein-kz/HiFormer

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