Title | Discrepancy-Based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images |
Author | |
DOI | |
Publication Years | 2022
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Conference Name | 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
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ISSN | 0302-9743
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EISSN | 1611-3349
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ISBN | 978-3-031-16451-4
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Source Title | |
Volume | 13438 LNCS
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Pages | 24-34
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Conference Date | SEP 18-22, 2022
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Conference Place | null,Singapore,SINGAPORE
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Publication Place | GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
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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
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Language | English
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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]
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WOS Research Area | Computer Science
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WOS Subject | Computer Science, Artificial Intelligence
; Computer Science, Theory & Methods
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WOS Accession No | WOS:000867418200003
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Scopus EID | 2-s2.0-85139008873
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Data Source | Scopus
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Citation statistics |
Cited Times [WOS]:1
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Document Type | Conference paper |
Identifier | http://kc.sustech.edu.cn/handle/2SGJ60CL/406281 |
Department | Department 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.
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