## 1 Introduction
For instance, Swin Transformer (Swin) uses a moving window attention mechanism to enhance inter-patch information flow, while CSWin Transformer (CSWin) utilizes cross-window attention to simultaneously capture both global and local details through an alternating combination of horizontal and vertical attention modules. Mobile-Former [26] introduces a novel parallel structure and a low cost two-way bridge, effectively integrating the strengths of MobileNet [27] and CNN while maintaining high-efficiency information capture capabilities. EdgeViT [28] presents a Local-Global-Local bottleneck combining three attention mechanisms, Local aggregation, Global sparse attention, and Local propagation, which reduce network parameters while preserving performance. MaxViT introduces a multi-axis attention mechanism that integrates Grid Attention and Block Attention, balancing the receptive field and computational resources. MINTFormer, proposed in this study, combines the CSWin and MaxViT architectures as its encoder. The cross-shaped window of CSWin captures global features within the feature map, while the multi-axis attention of MaxViT captures information among neighboring pixels. The function of skip connections is to provide supplementary information to the feature map for the decoder, which often lacks sufficient depth in its features. In U-Net, feature maps at different stages are directly added via skip connections. While this approach provides the missing features at the same level in the decoder, it fails to allow a shallow decoder to access information present in a deep feature map. The U-Net++ [29] and U-Net3+ [30] models introduce nested skip connections into the U-Net architecture, enabling the shallow decoder to access information from the deep feature map. Building on this, Missformer [31] enhances sub-features within the feature map by strengthening the context bridge, allowing the encoder to supplement feature map details without compromising the original information. However, visualization of the encoder's output reveals that the encoded feature map contains discernible redundant texture information. Directly injecting the encoder's output into the decoder would impede the decoder's upsampling operation due to this redundancy. Therefore, the feature map produced by the encodermust be filtered before being injected into the decoder This study proposes incorporating a Demodulate Bridge within MINTFormer, which consists of a series of convolutional operations of varying dimensions and scales for the feature maps of different stages and sizes. These operations demodulate the feature maps generated by MaxViT and CSWin, amalgamate them into a unified feature map, and filter out redundant texture information. The decoder's role is to utilize profound semantic data provided by the encoder to delineate the position of lesions on the feature map, while also supplementing contextual information during upsampling. U-Net and its variants employ an overlap-tile strategy in the decoder to supplement contextual information. This strategy divides the feature map into multiple patches and supplements the contextual information of the patch currently requiring scaling by using the surrounding patches' data. Ultimately, all magnified patches are integrated to create the upsampled feature map. While this strategy effectively utilizes neighboring pixel data within a patch, dividing the feature map into multiple patches hinders complete information exchange between patches. Missformer incorporates a shift window mechanism into the decoder, facilitating information sharing between patches. However, the fixed size of the moving window only captures information about organs matching its size. If the organs exceed the dimensions of the moving window or are considerably smaller, the window fails to capture multi-scale organ information. Therefore, it is necessary to design a moving window of varying sizes for the decoder to capture multi-scale organ information.