中文版 | English
Title

Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images

Author
DOI
Publication Years
2022
Conference Name
25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
ISSN
0302-9743
EISSN
1611-3349
ISBN
978-3-031-16451-4
Source Title
Volume
13438 LNCS
Pages
24-34
Conference Date
SEP 18-22, 2022
Conference Place
null,Singapore,SINGAPORE
Publication Place
GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
Publisher
Abstract
Weakly supervised methods, such as class activation maps (CAM) based, have been applied to achieve bleeding segmentation with low annotation efforts in Wireless Capsule Endoscopy (WCE) images. However, the CAM labels tend to be extremely noisy, and there is an irreparable gap between CAM labels and ground truths for medical images. This paper proposes a new Discrepancy-basEd Active Learning (DEAL) approach to bridge the gap between CAMs and ground truths with a few annotations. Specifically, to liberate labor, we design a novel discrepancy decoder model and a CAMPUS (CAM, Pseudo-label and groUnd-truth Selection) criterion to replace the noisy CAMs with accurate model predictions and a few human labels. The discrepancy decoder model is trained with a unique scheme to generate standard, coarse and fine predictions. And the CAMPUS criterion is proposed to predict the gaps between CAMs and ground truths based on model divergence and CAM divergence. We evaluate our method on the WCE dataset and results show that our method outperforms the state-of-the-art active learning methods and reaches comparable performance to those trained with full annotated datasets with only 10% of the training data labeled. The source code is available at https://github.com/baifanxxx/DEAL.
Keywords
SUSTech Authorship
Others
Language
English
URL[Source Record]
Indexed By
Funding Project
National Key R&D program of China[2019YFB1312400] ; Hong Kong RGC CRF[C4063-18G] ; Hong Kong RGC GRF[14211420]
WOS Research Area
Computer Science
WOS Subject
Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS Accession No
WOS:000867418200003
Scopus EID
2-s2.0-85139008873
Data Source
Scopus
Citation statistics
Cited Times [WOS]:1
Document TypeConference paper
Identifierhttp://kc.sustech.edu.cn/handle/2SGJ60CL/406281
DepartmentDepartment of Electrical and Electronic Engineering
Affiliation
1.Department of Electronic Engineering,The Chinese University of Hong Kong,Shatin,Hong Kong
2.Department of Electrical Engineering,City University of Hong Kong,Kowloon,Hong Kong
3.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,China
Recommended Citation
GB/T 7714
Bai,Fan,Xing,Xiaohan,Shen,Yutian,et al. Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:24-34.
Files in This Item:
There are no files associated with this item.
Related Services
Fulltext link
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Export to Excel
Export to Csv
Altmetrics Score
Google Scholar
Similar articles in Google Scholar
[Bai,Fan]'s Articles
[Xing,Xiaohan]'s Articles
[Shen,Yutian]'s Articles
Baidu Scholar
Similar articles in Baidu Scholar
[Bai,Fan]'s Articles
[Xing,Xiaohan]'s Articles
[Shen,Yutian]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Bai,Fan]'s Articles
[Xing,Xiaohan]'s Articles
[Shen,Yutian]'s Articles
Terms of Use
No data!
Social Bookmark/Share
No comment.

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.