A Generative Model Based Approach to Retrieving Ischemic Stroke Images

Publication Type

Conference Proceeding Article

Publication Date

12-2011

Abstract

This paper proposes a generative model approach to automatically annotate medical images to improve the efficiency and effectiveness of image retrieval systems for teaching, research, and diagnosis. The generative model captures the probabilistic relationships among relevant classification tags, tentative lesion patterns, and selected input features. Operating on the imperfect segmentation results of input images, the probabilistic framework can effectively handle the inherent uncertainties in the images and insufficient information in the training data. Preliminary assessment in the ischemic stroke subtype classification shows that the proposed system is capable of generating the relevant tags for ischemic stroke brain images. The main benefit of this approach is its scalability; the method can be applied in large image databases as it requires only minimal manual labeling of the training data and does not demand high-precision segmentation of the images.

Discipline

Computer Sciences | Health Information Technology

Publication

AMIA Annual Symposium Proceedings, 2011

Volume

2011

First Page

312

Last Page

321

ISBN

1942-597X

City or Country

Washington DC, USA

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