Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor
  Segmentation
Ziya Ata Yazıcı İlkay Öksüz Hazım Kemal Ekenel
Glioblastoma is a highly aggressive and malignant brain tumor type that
requires early diagnosis and prompt intervention. Due to its heterogeneity in
appearance, developing automated detection approaches is challenging. To
address this challenge, Artificial Intelligence (AI)-driven approaches in
healthcare have generated interest in efficiently diagnosing and evaluating
brain tumors. The Brain Tumor Segmentation Challenge (BraTS) is a platform for
developing and assessing automated techniques for tumor analysis using
high-quality, clinically acquired MRI data. In our approach, we utilized a
multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to
perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET),
Tumor Core (TC), and Whole Tumor (WT). The multi-scale feature extraction
provides better contextual feature aggregation in high resolutions and the Swin
Transformer blocks improve the global feature extraction at deeper levels of
the model. The segmentation mask generation in the decoder branch is guided by
the attention-refined features gathered from the encoder branch to enhance the
important attributes. Moreover, hierarchical supervision is used to train the
model efficiently. Our model's performance on the validation set resulted in
92.19, 87.75, and 83.18 Dice Scores and 89.09, 84.67, and 82.15 Lesion-wise
Dice Scores in WT, TC, and ET, respectively. The code is publicly available at
https://github.com/yaziciz/GLIMS.