Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
CoRR(2024)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要