FCSE at Medical Tasks of ImageCLEF 2013
Date Issued
2013-09-23
Author(s)
Abstract
This paper presents the details of the participation of FCSE (Faculty of Computer Science and Engineering) research team in ImageCLEF 2013 medical tasks (modality classification, ad-hoc image retrieval and case-based retrieval). For the modality classification task we used SIFT descriptors and tf − idf weights of the surrounding text (image caption and paper title) as features. SVMs with χ
2 kernel and one-vsall strategy were used as classifiers. For the ad-hoc image retrieval task
and case-based retrieval we adopted a strategy which uses a combination
of word-space and concept-space approaches. The word-space approach
uses the Terrier IR search engine to index and retrieve the text associated with the images/cases. The concept-space approach uses Metamap to map the text data into a set of UMLS (Unified Medical Language System) concepts, which are later indexed and retrieved by the Terrier
IR search engine. The results from the word-space and concept-space retrieval are fused using linear combination. For the compound figure separation task, we used unsupervised algorithm based on breadth-first search strategy using only visual information from the medical images.
The selected algorithms were tuned and tested on the data from ImageCLEF 2012 medical task and based on the selected parameters we submitted the new experiments for ImageCLEF 2013 medical task. We achieved very good overall performance: the best run for the modality classification ranked 2nd in the overall score, the best run for the ad-hoc image retrieval ranked 3rd.
2 kernel and one-vsall strategy were used as classifiers. For the ad-hoc image retrieval task
and case-based retrieval we adopted a strategy which uses a combination
of word-space and concept-space approaches. The word-space approach
uses the Terrier IR search engine to index and retrieve the text associated with the images/cases. The concept-space approach uses Metamap to map the text data into a set of UMLS (Unified Medical Language System) concepts, which are later indexed and retrieved by the Terrier
IR search engine. The results from the word-space and concept-space retrieval are fused using linear combination. For the compound figure separation task, we used unsupervised algorithm based on breadth-first search strategy using only visual information from the medical images.
The selected algorithms were tuned and tested on the data from ImageCLEF 2012 medical task and based on the selected parameters we submitted the new experiments for ImageCLEF 2013 medical task. We achieved very good overall performance: the best run for the modality classification ranked 2nd in the overall score, the best run for the ad-hoc image retrieval ranked 3rd.
Subjects
File(s)![Thumbnail Image]()
Loading...
Name
CLEF2013wn-ImageCLEF-KitanovskiEt2013.pdf
Size
434.05 KB
Format
Adobe PDF
Checksum
(MD5):9c00b7cbfbd915868785c8a52beec5e3