Multibranch Learning for Angiodysplasia Segmentation with Attention-Guided Networks and Domain Adaptation

Xiao Jia1; Xiaochun Mai1; Xiaohan Xing1; Yutian Shen1; Jiankun Wang2; Max Q.-H. Meng1,2

10.1109/ICRA48506.2021.9562100

2021

IEEE International Conference on Robotics and Automation

1050-4729

2577-087X

978-1-7281-9078-5

2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021)

2021-May

12373-12379

2021.5.31-2021.6.4

Xi'an, China

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

As a common cause of anemia and gastrointestinal bleeding, angiodysplasia (AD) diagnosis in wireless capsule endoscopy (WCE) images is important in clinical. Current manual review requires undivided concentration of the gastroenterologists, which is laborious and time-consuming. The development of computational methods that can assist automated diagnosis of angiodysplasia is highly desirable. In this paper, we present a new approach, ADNet, for angiodysplasia segmentation using convolutional neural networks (CNNs). Compared with previous learning strategies, ADNet gains accuracy from attentionguided and domain-adversarial training via a multibranch CNN architecture. Specifically, the core branch is constructed for AD segmentation in a fully convolutional manner. Then we propose an attention module embedded in the attention branch to enhance network feature learning, which allows ADNet to focus on the most informative and AD relevant regions while processing. Furthermore, an adaptation branch is built to learn domain-invariant features by adversarial training, aiming to improve the performance when datasets are expanded while preventing the degradation induced by the variations in WCE image acquisition. ADNet is evaluated using two WCE datasets with angiodysplasia and the results show the accuracy gains we obtain, where the state-of-the-art segmentation performance on the public dataset of GIANA'17 is achieved.

Training Wireless communication Image segmentation Computer architecture Manuals Gastroenterology Gastrointestinal tract

其他

英语

CPCI-S ; EI

National Key R&D program of China[2019YFB1312400]

Automation & Control Systems ; Robotics

Automation & Control Systems ; Robotics

WOS:000771405404057

20220911737350

Convolution ; Endoscopy ; Image enhancement ; Learning systems ; Medical imaging

Biomedical Engineering:461.1 ; Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Imaging Techniques:746

人工提交

1.The Chinese University of Hong Kong
2.Southern University of Science and Technology

Xiao Jia,Xiaochun Mai,Xiaohan Xing,et al. Multibranch Learning for Angiodysplasia Segmentation with Attention-Guided Networks and Domain Adaptation[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2021:12373-12379.