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Title: Automatic pathology annotation on medical images: A statistical machine translation framework
Authors: Gong, T.
Li, S. 
Tan, C.L. 
Pang, B.C.
Lim, C.C.T.
Lee, C.K.
Tian, Q.
Zhang, Z.
Issue Date: 2010
Citation: Gong, T.,Li, S.,Tan, C.L.,Pang, B.C.,Lim, C.C.T.,Lee, C.K.,Tian, Q.,Zhang, Z. (2010). Automatic pathology annotation on medical images: A statistical machine translation framework. Proceedings - International Conference on Pattern Recognition : 2504-2507. ScholarBank@NUS Repository.
Abstract: Large number of medical images are produced daily in hospitals and medical institutions, the needs to efficiently process, index, search and retrieve these images are great. In this paper, we propose a pathology-based medical image annotation framework using a statistical machine translation approach. After pathology terms and regions of interest (ROIs) are extracted from training texts and images respectively, we use a statistical machine translation model to iteratively learn the alignments between the ROIs and the pathology terms and generate an ROI-pathology translation table. In testing, we annotate the ROIs in testing image with pathology of the highest probability in the translation table. The overall annotation performance are promising to doctors and medical professionals. © 2010 IEEE.
Source Title: Proceedings - International Conference on Pattern Recognition
ISBN: 9780769541099
ISSN: 10514651
DOI: 10.1109/ICPR.2010.613
Appears in Collections:Staff Publications

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