AIRP‐UNet: A Multimodal Brain Tumour Segmentation Method Based on Attention‐Guided Inverted Residual Pyramid Network

ABSTRACT

Accurate glioma segmentation from multimodal MRI is essential, yet existing methods struggle with inconsistencies, noise, and high tumour variability. To address this, we propose AIRP‑UNet, an asymmetric hybrid 2D network. Its residual‑block‑based encoder stably extracts hierarchical features and alleviates gradient vanishing; the atrous spatial pyramid pooling bottleneck captures multi‑scale context via atrous convolutions; the attention‑gated inverted residual decoder suppresses fusion redundancy, enhancing boundary adherence and small‑region consistency. Our contribution is design‑analytical: we deliberately break the encoder–decoder symmetry conventionally assumed in U‑shaped networks and report controlled evidence for the three design decisions. On BraTS2021 with patient‑level five‑fold cross‑validation, we achieve an average Dice of 90.10% ± 0.91% and HD95 of 2.45 ± 0.43 mm, demonstrating a competitive accuracy–efficiency trade‑off and serving as a reproducible reference for 2.5D/3D extensions.