한국정보처리학회:학술대회논문집 (Proceedings of the Korea Information Processing Society Conference)

한국정보처리학회 (Korea Information Processing Society)
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뇌종양 분할을 위한 3D 이중 융합 주의 네트워크

3D Dual-Fusion Attention Network for Brain Tumor Segmentation

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  • 김수형 (전남대학교 인공지능융합학과)
  • Hoang-Son Vo-Thanh (Dept. of Information Technology, HCMC University of Foreign Language Information Technology) ;
  • Tram-Tran Nguyen Quynh (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Nhu-Tai Do (Dept. of Artificial Intelligence Convergence, Chonnam National University) ;
  • Soo-Hyung Kim (Dept. of Artificial Intelligence Convergence, Chonnam National University)
  • 발행 : 2023.05.18
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초록

Brain tumor segmentation problem has challenges in the tumor diversity of location, imbalance, and morphology. Attention mechanisms have recently been used widely to tackle medical segmentation problems efficiently by focusing on essential regions. In contrast, the fusion approaches enhance performance by merging mutual benefits from many models. In this study, we proposed a 3D dual fusion attention network to combine the advantages of fusion approaches and attention mechanisms by residual self-attention and local blocks. Compared to fusion approaches and related works, our proposed method has shown promising results on the BraTS 2018 dataset.

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과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1I1A3A04036408) and also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2021-0-02068, Artificial Intelligence Innovation Hub).