@inproceedings{13c61f4860004b3d8bf08cf945a1d9c8,
title = "DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation",
abstract = "Transformer-based models have been widely demonstrated to be successful in computer vision tasks by modeling long-range dependencies and capturing global representations. However, they are often dominated by features of large patterns leading to the loss of local details (e.g., boundaries and small objects), which are critical in medical image segmentation. To alleviate this problem, we propose a Dual-Aggregation Transformer Network called DuAT, which is characterized by two innovative designs, namely, the Global-to-Local Spatial Aggregation (GLSA) and Selective Boundary Aggregation (SBA) modules. The GLSA has the ability to aggregate and represent both global and local spatial features, which are beneficial for locating large and small objects, respectively. The SBA module aggregates the boundary characteristic from low-level features and semantic information from high-level features for better-preserving boundary details and locating the re-calibration objects. Extensive experiments in six benchmark datasets demonstrate that our proposed model outperforms state-of-the-art methods in the segmentation of skin lesion images and polyps in colonoscopy images. In addition, our approach is more robust than existing methods in various challenging situations, such as small object segmentation and ambiguous object boundaries. The project is available at https://github.com/Barrett-python/DuAT.",
keywords = "Dual decoder, Polyp segmentation, Vision Transformers",
author = "Feilong Tang and Zhongxing Xu and Qiming Huang and Jinfeng Wang and Xianxu Hou and Jionglong Su and Jingxin Liu",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023 ; Conference date: 13-10-2023 Through 15-10-2023",
year = "2024",
doi = "10.1007/978-981-99-8469-5_27",
language = "English",
isbn = "9789819984688",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "343--356",
editor = "Qingshan Liu and Hanzi Wang and Rongrong Ji and Zhanyu Ma and Weishi Zheng and Hongbin Zha and Xilin Chen and Liang Wang",
booktitle = "Pattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings",
}