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This work aims to improve medical image segmentation by combining the strengths of CNNs and Transformers in a lightweight framework.

May 2, 2026

A hybrid CNN-Transformer network integrating multiscale spatially detailed features for medical image segmentation.

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Randomized trial demonstrates enhanced accuracy in image segmentation using a hybrid CNN-Transformer model, indicating its clinical relevance.

This work aims to improve medical image segmentation by combining the strengths of CNNs and Transformers in a lightweight framework.
Proposed a novel encoder integrating parallel CNN and Swin Transformer architectures.
Developed the Semantics and Detail Infusion (SDI) module for fusing multi-scale features using attention.
Evaluated on the Synapse Multi-Organ Segmentation and Aortic Vessel Tree datasets.
Achieved Dice coefficients of 84.19% and 87.91% for the two datasets.
Reported Hausdorff Distances of 12.64 mm and 7.06 mm, respectively.
Gained 7.34% and 5.02% improvements over the UNet benchmark in Dice scores.

Li et al. (2026) studied this question.

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Randomized trial demonstrates enhanced accuracy in image segmentation using a hybrid CNN-Transformer model, indicating its clinical relevance.