  Transformers have shown great success in medical image segmentation. However,
transformers may exhibit a limited generalization ability due to the underlying
single-scale self-attention (SA) mechanism. In this paper, we address this
issue by introducing a Multi-scale hiERarchical vIsion Transformer (MERIT)
backbone network, which improves the generalizability of the model by computing
SA at multiple scales. We also incorporate an attention-based decoder, namely
Cascaded Attention Decoding (CASCADE), for further refinement of multi-stage
features generated by MERIT. Finally, we introduce an effective multi-stage
feature mixing loss aggregation (MUTATION) method for better model training via
implicit ensembling. Our experiments on two widely used medical image
segmentation benchmarks (i.e., Synapse Multi-organ, ACDC) demonstrate the
superior performance of MERIT over state-of-the-art methods. Our MERIT
architecture and MUTATION loss aggregation can be used with downstream medical
image and semantic segmentation tasks.
