NDLI: Label the many with a few: Semi-automatic medical image modality discovery in a large image collection

Content Provider	IEEE Xplore Digital Library
Author	Vajda, Szilard You, Daekeun Antani, Sameer K. Thoma, George R.
Copyright Year	2014
Description	Author affiliation: Lister Hill National Center for Biomedical Communications, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD, USA (Vajda, Szilard; Antani, Sameer K.; Thoma, George R.) \|\| University of Michigan Health System, 1500 E Medical Center Dr, Ann Arbor, USA (You, Daekeun)
Abstract	In this paper we present a fast and effective method for labeling images in a large image collection. Image modality detection has been of research interest for querying multimodal medical documents. To accurately predict the different image modalities using complex visual and textual features, we need advanced classification schemes with supervised learning mechanisms and accurate training labels. Our proposed method, on the other hand, uses a multiview-approach and requires minimal expert knowledge to semi-automatically label the images. The images are first projected in different feature spaces, and are then clustered in an unsupervised manner. Only the cluster representative images are labeled by an expert. Other images from the cluster “inherit” the labels from these cluster representatives. The final label assigned to each image is based on a voting mechanism, where each vote is derived from different feature space clustering. Through experiments we show that using only 0.3% of the labels was sufficient to annotate 300,000 medical images with 49.95% accuracy. Although, automatic labeling is not as precise as manual, it saves approximately 700 hours of manual expert labeling, and may be sufficient for next-stage classifier training. We find that for this collection accuracy improvements are feasible with better disparate feature selection or different filtering mechanisms.
Starting Page	167
Ending Page	173
File Size	1538813
Page Count	7
File Format	PDF
ISBN	9781479945276
DOI	10.1109/CICARE.2014.7007850
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Computed tomography Manuals Feature extraction Labeling X-ray imaging Biomedical imaging
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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