Hierarchical Annotation of Medical Images

author: Ivica Dimitrovski, Department of Knowledge Technologies, Jožef Stefan Institute
published: Nov. 7, 2008,   recorded: October 2008,   views: 383
Categories

Slides

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Bibliography

Description

In this paper, we describe an approach for the automatic medical annotation task of the 2008 CLEF cross-language image retrieval campaign (ImageCLEF). The data comprise 12076 fully annotated images according to the IRMA code. This work is focused on the process of feature extraction from images and hierarchical multi-label classification. To extract features from the images we used a technique called: local distribution of edges.

With this techniques each image was described with 80 variables. The goal of the classification task was to classify an image according to the IRMA code. The IRMA code is organized hierarchically. Hence, as classifer we selected an extension of the predictive clustering trees (PCTs) that is able to handle this type of data.

Further more, we constructed ensembles (Bagging and Random Forests) that use PCTs as base classifiers.

See Also:

Download slides icon Download slides: sikdd08_dimitrovski_hami_01.pdf (821.8 KB)

Download slides icon Download slides: sikdd08_dimitrovski_hami_01.ppt (1.9 MB)

Download article icon Download article: sikdd08_dimitrovski_hami_article.pdf (227.3 KB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: