NDLI: Active labeling: Application to wireless endoscopy analysis

Content Provider	IEEE Xplore Digital Library
Author	Radeva, P. Drozdzal, M. Segui, S. Igual, L. Malagelada, C. Azpiroz, F. Vitria, J.
Copyright Year	2012
Description	Author affiliation: Department of Applied Mathematics and Analysis, Universitat de Barcelona, Spain (Radeva, P.; Drozdzal, M.; Segui, S.; Igual, L.; Vitria, J.) \|\| Digestive System Research Unit, Hospital de Vall d'Hebron, Spain (Malagelada, C.; Azpiroz, F.)
Abstract	Today, robust learners trained in a real supervised machine learning application should count with a rich collection of positive and negative examples. Although in many applications, it is not difficult to obtain huge amount of data, labeling those data can be a very expensive process, especially when dealing with data of high variability and complexity. A good example of such cases are data from medical imaging applications where annotating anomalies like tumors, polyps, atherosclerotic plaque or informative frames in wireless endoscopy need highly trained experts. Building a representative set of training data from medical videos (e.g. Wireless Capsule Endoscopy) means that thousands of frames to be labeled by an expert. It is quite normal that data in new videos come different and thus are not represented by the training set. In this paper, we review the main approaches on active learning and illustrate how active learning can help to reduce expert effort in constructing the training sets. We show that applying active learning criteria, the number of human interventions can be significantly reduced. The proposed system allows the annotation of informative/non-informative frames of Wireless Capsule Endoscopy video containing more than 30000 frames each one with less than 100 expert ”clicks”.
Starting Page	174
Ending Page	181
File Size	2435811
Page Count	8
File Format	PDF
ISBN	9781467323598
e-ISBN	9781467323628
DOI	10.1109/HPCSim.2012.6266908
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-02
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Wireless communication Uncertainty Interactive labeling Endoscopes Active learning Machine learning WCE Labeling Videos
Content Type	Text
Resource Type	Article

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