Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation
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arXiv
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2025
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| author | Moodi, Farzan Shoushtari, Fereshteh Khodadadi Valizadeh, Gelareh Mazinani, Dornaz Salari, Hanieh Mobarak Rad, Hamidreza Saligheh |
| author_facet | Moodi, Farzan Shoushtari, Fereshteh Khodadadi Valizadeh, Gelareh Mazinani, Dornaz Salari, Hanieh Mobarak Rad, Hamidreza Saligheh |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20446 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation Moodi, Farzan Shoushtari, Fereshteh Khodadadi Valizadeh, Gelareh Mazinani, Dornaz Salari, Hanieh Mobarak Rad, Hamidreza Saligheh Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition 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. |
| title | Attention Xception UNet (AXUNet): A Novel Combination of CNN and Self-Attention for Brain Tumor Segmentation |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.20446 |