DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation

Feilong Tang, Zhongxing Xu, Qiming Huang, Jinfeng Wang, Xianxu Hou, Jionglong Su*, Jingxin Liu

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationPattern Recognition and Computer Vision - 6th Chinese Conference, PRCV 2023, Proceedings
EditorsQingshan Liu, Hanzi Wang, Rongrong Ji, Zhanyu Ma, Weishi Zheng, Hongbin Zha, Xilin Chen, Liang Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages343-356
Number of pages14
ISBN (Print)9789819984688
DOIs
Publication statusPublished - 2024
Event6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023 - Xiamen, China
Duration: 13 Oct 202315 Oct 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14429 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th Chinese Conference on Pattern Recognition and Computer Vision, PRCV 2023
Country/TerritoryChina
CityXiamen
Period13/10/2315/10/23

Keywords

  • Dual decoder
  • Polyp segmentation
  • Vision Transformers

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