  Since their emergence, Convolutional Neural Networks (CNNs) have made
significant strides in medical image analysis. 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 in medical image segmentation due to their
ability to process global features effectively. The scalability issues of the
self-attention mechanism and lack of the CNN-like inductive bias may have
limited their adoption. Therefore, hybrid Vision transformers
(CNN-Transformer), exploiting the advantages of both Convolution and
Self-attention Mechanisms, have gained importance. In this work, we present
MaxViT-UNet, a new Encoder-Decoder based UNet type hybrid vision transformer
(CNN-Transformer) for medical image segmentation. The proposed Hybrid Decoder
is designed to harness the power of both the convolution and self-attention
mechanisms at each decoding stage with a nominal memory and computational
burden. The inclusion of multi-axis self-attention, within each decoder stage,
significantly enhances the discriminating capacity between the object and
background regions, thereby helping in improving the segmentation efficiency.
In the Hybrid Decoder, a new block is also proposed. 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 segment the nuclei regions progressively.
Experimental results on MoNuSeg18 and MoNuSAC20 datasets demonstrate the
effectiveness of the proposed technique.
