Diagnose like a Clinician: Third-order attention guided lesion amplification network for WCE image classification

Xing，Xiaohan1; Yuan，Yixuan2; Meng，Max Q.H.1,3,4

10.1109/IROS45743.2020.9340750

2020-10-24

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

2153-0858

2153-0866

978-1-7281-6213-3

IEEE International Conference on Intelligent Robots and Systems

10145-10151

OCT 24-JAN 24, 2020-2021

null,null,ELECTR NETWORK

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

IEEE

Wireless capsule endoscopy (WCE) is a novel imaging tool that allows the noninvasive visualization of the entire gastrointestinal (GI) tract without causing discomfort to the patients. Although convolutional neural networks (CNNs) have obtained promising performance for the automatic lesion recognition, the results of the current approaches are still limited due to the small lesions and the background interference in the WCE images. To overcome these limits, we propose a Third-order Attention guided Lesion Amplification Network (TALA-Net) for WCE image classification. The TALA-Net consists of two branches, including a global branch and an attention-aware branch. Specifically, taking the high-level features in the global branch as the input, we propose a Third-order Attention (ToA) module to generate attention maps that can indicate potential lesion regions. Then, an Attention Guided Lesion Amplification (AGLA) module is proposed to deform multiple level features in the global branch, so as to zoom in the potential lesion features. The deformed features are fused into the attention-aware branch to achieve finer-scale lesion recognition. Finally, predictions from the global and attention-aware branches are averaged to obtain the classification results. Extensive experiments show that the proposed TALA-Net outperforms state-of-the-art methods with an overall classification accuracy of 94.72% on the WCE dataset.

Wireless communication Visualization Interference Tools Lesions Task analysis Image classification

其他

英语

EI ; CPCI-S

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

Automation & Control Systems ; Computer Science ; Engineering ; Robotics

Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Robotics

WOS:000724145800014

20211110064085

Agricultural robots ; Classification (of information) ; Convolutional neural networks ; Endoscopy ; Intelligent robots

Medicine and Pharmacology:461.6 ; Information Theory and Signal Processing:716.1 ; Robot Applications:731.6

2-s2.0-85102413760

Scopus

1.The Chinese University of Hong Kong,Department of Electronic Engineering,Hong Kong,Hong Kong
2.City University of Hong Kong,Department of Electrical Engineering,Hong Kong,Hong Kong
3.Southern University of Science and Technology,Department of Electronic and Electrical Engineering,Shenzhen,China
4.The Shenzhen Research Institute,Chinese University of Hong Kong,Shenzhen,China

Xing，Xiaohan,Yuan，Yixuan,Meng，Max Q.H.. Diagnose like a Clinician: Third-order attention guided lesion amplification network for WCE image classification[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2020:10145-10151.