Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation
Journal:
arXiv
Published Date:
Mar 26, 2025
Abstract
Accurate segmentation of glioma brain tumors is crucial for diagnosis and
treatment planning. Deep learning techniques offer promising solutions, but
optimal model architectures remain under investigation. We used the BraTS 2021
dataset, selecting T1 with contrast enhancement (T1CE), T2, and
Fluid-Attenuated Inversion Recovery (FLAIR) sequences for model development.
The proposed Attention Xception UNet (AXUNet) architecture integrates an
Xception backbone with dot-product self-attention modules, inspired by
state-of-the-art (SOTA) large language models such as Google Bard and OpenAI
ChatGPT, within a UNet-shaped model. We compared AXUNet with SOTA models.
Comparative evaluation on the test set demonstrated improved results over
baseline models. Inception-UNet and Xception-UNet achieved mean Dice scores of
90.88 and 93.24, respectively. Attention ResUNet (AResUNet) attained a mean
Dice score of 92.80, with the highest score of 84.92 for enhancing tumor (ET)
among all models. Attention Gate UNet (AGUNet) yielded a mean Dice score of
90.38. AXUNet outperformed all models with a mean Dice score of 93.73. It
demonstrated superior Dice scores across whole tumor (WT) and tumor core (TC)
regions, achieving 92.59 for WT, 86.81 for TC, and 84.89 for ET. The
integration of the Xception backbone and dot-product self-attention mechanisms
in AXUNet showcases enhanced performance in capturing spatial and contextual
information. The findings underscore the potential utility of AXUNet in
facilitating precise tumor delineation.
