Automatic Pathology Annotation on Medical Images: A Statistical Machine Translation Framework
| Citations: | 1 - 1 self |
BibTeX
@MISC{Gong_automaticpathology,
author = {Tianxia Gong and Shimiao Li and Chew Lim Tan and Boon Chuan Pang and C. C. Tchoyoson Lim and Cheng Kiang Lee and Qi Tian and Zhuo Zhang},
title = {Automatic Pathology Annotation on Medical Images: A Statistical Machine Translation Framework},
year = {}
}
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
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. 1.
Citations
| 892 | Mathematics of Statistical Machine Translation: Parameter Estimation - Brown, Pietra, et al. - 1993 |
| 502 | A statistical approach to machine translation - Brown, Cocke, et al. - 1990 |
| 327 | Object recognition as machine translation :learning a lexicon for a fixed image vocabulary - Duygulu, Barnard, et al. - 2002 |
| 145 | Effective mapping of biomedical text to the umls metathesaurus: the metamap program - Aronson |
| 86 | A review of content-based image retrieval systems in medical applications-clinical benefits and future directions - Muller, Michoux, et al. - 2004 |
| 10 | The umls metathesaurus: representing different views of biomedical concepts - Schuyler, Hole, et al. - 1993 |
| 2 | Tbidoc:3d content-based ct image retrieval system for traumatic brain injury - Li, Gong, et al. - 2010 |
| 2 | Overview of the clef 2009 medical image annotation track,” CLEF working notes - Tommasi, Caputo, et al. - 2009 |
