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Second glance framework (secG): enhanced ulcer detection with deep learning on a large wireless capsule endoscopy dataset
Author(s): Sen Wang; Yuxiang Xing; Li Zhang; Hewei Gao; Hao Zhang
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Paper Abstract

Wireless Capsule Endoscopy (WCE) enables physicians to examine gastrointestinal (GI) tract without surgery. It has become a widely used diagnostic technique while the huge image data brings heavy burden to doctors. As a result, computer-aided diagnosis systems that can assist doctors as a second observer gain great research interest. In this paper, we aim to demonstrate the feasibility of deep learning for lesion recognition. We propose a Second Glance framework for ulcer detection and verified its effectiveness and robustness on a large ulcer WCE dataset (largest one to our knowledge for this problem) which consists of 1,504 independent WCE videos. The performance of our method is compared with off-the-shelf detection frameworks. Our framework achieves the best ROC-AUC of 0.9235 and outperforms the results of RetinaNet (0.8901), Faster-RCNN(0.9038) and SSD-300 (0.8355).

Paper Details

Date Published: 31 July 2019
PDF: 7 pages
Proc. SPIE 11198, Fourth International Workshop on Pattern Recognition, 111980V (31 July 2019); doi: 10.1117/12.2540456
Show Author Affiliations
Sen Wang, Tsinghua Univ. (China)
Yuxiang Xing, Tsinghua Univ. (China)
Li Zhang, Tsinghua Univ. (China)
Hewei Gao, Tsinghua Univ. (China)
Hao Zhang, Ankon Technologies Co., Ltd. (China)


Published in SPIE Proceedings Vol. 11198:
Fourth International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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