Convolutional Neural Networks (CNNs) have made significant strides in medical image analysis in recent years. However, the local nature of the convolution operator may pose a limitation for capturing global and long-range interactions in CNNs. Recently, Transformers have gained popularity in the computer vision community and also medical image segmentation due to their ability to process global features effectively. The scalability issues of self-attention mechanism and lack of the CNN-like inductive bias may have limited their adoption. Therefore, hybrid Vision transformers (CNN-Transformer), exploiting advantages of both Convolution and Self-attention Mechanisms, have gained importance. In this work, we present MaxViT-UNet, an Encoder-Decoder based hybrid vision transformer (CNN-Transformer) for medical image segmentation. The proposed Hybrid Decoder, based on MaxViT-block, is designed to harness the power of both the convolution and self-attention mechanisms at each decoding stage with nominal computational burden. The inclusion of multi-axis self-attention, within each decoder stage, significantly enhances the discriminating capacity between the object and background regions, and thereby helps in improving the segmentation efficiency. In the Hybrid Decoder block, the fusion process commences by integrating the upsampled lower level decoder features, obtained through transpose convolution, with the skip-connection features derived from the hybrid encoder. Subsequently, the fused features undergo refinement through the utilization of a multi-axis attention mechanism. The proposed decoder block is repeated multiple times to progressively segment the nuclei regions. Experimental results on MoNuSeg18 and MoNuSAC20 dataset demonstrates the effectiveness of the proposed technique.

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