  The Transformer has been successfully used in medical image segmentation due
to its excellent long-range modeling capabilities. However, patch segmentation
is necessary when building a Transformer class model. This process may disrupt
the tissue structure in medical images, resulting in the loss of relevant
information. In this study, we proposed a Heterogeneous Swin Transformer with
Multi-Receptive Field (HST-MRF) model based on U-shaped networks for medical
image segmentation. The main purpose is to solve the problem of loss of
structural information caused by patch segmentation using transformer by fusing
patch information under different receptive fields. The heterogeneous Swin
Transformer (HST) is the core module, which achieves the interaction of
multi-receptive field patch information through heterogeneous attention and
passes it to the next stage for progressive learning. We also designed a
two-stage fusion module, multimodal bilinear pooling (MBP), to assist HST in
further fusing multi-receptive field information and combining low-level and
high-level semantic information for accurate localization of lesion regions. In
addition, we developed adaptive patch embedding (APE) and soft channel
attention (SCA) modules to retain more valuable information when acquiring
patch embedding and filtering channel features, respectively, thereby improving
model segmentation quality. We evaluated HST-MRF on multiple datasets for polyp
and skin lesion segmentation tasks. Experimental results show that our proposed
method outperforms state-of-the-art models and can achieve superior
performance. Furthermore, we verified the effectiveness of each module and the
benefits of multi-receptive field segmentation in reducing the loss of
structural information through ablation experiments.
