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Article · Zenodo · September 1, 2026

COMPARING ENCODER FUSION STRATEGIES IN HYBRID CNN–TRANSFORMER NETWORKS FOR 3D BRAIN TUMOR SEGMENTATION: A CONTROLLED ABLATION ON BRATS 2021

Temurbek Abdullayev, Abdukadirova Gulbahor

Temurbek Abdullayev
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Abstract

Hybrid CNN–Transformer architectures have become the dominant paradigm for volumetric medical image segmentation, combining the local inductive bias of convolutions with the long-range context modeling of self-attention. However, the point at which the two encoder streams are fused and the mechanism used to fuse them are rarely studied in isolation: most published gains conflate fusion design with unrelated changes in depth, patch size, or training schedule. This paper presents a controlled ablation that isolates the fusion strategy as the sole independent variable. Using a shared CNN and Swin-Transformer backbone, we compare six fusion strategies—early, late, additive, concatenation, cross-attention, and a proposed gated multi-scale cross-attention fusion—on the BraTS 2021 dataset (1,251 training cases) under identical training conditions. The proposed gated multi-scale cross-attention fusion achieves the best overall performance, with mean Dice scores of 94.2%, 89.1%, and 82.5% for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET) sub-regions, respectively, and the lowest HD95 of 4.68 mm, outperforming the strongest single-branch baseline by 2.4–4.1 Dice points while adding only 12 million parameters.

Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesAdvanced Neural Network ApplicationsCNN-Transformer hybridcross-attention fusionSwin Transformer

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