A Structured Visual Learning Approach Mixed with Ontology Dimensions for Medical Queries

Chevallet, Jean–Pierre; Lim, Joo‐Hwee; Radhouani, Saïd

doi:10.1007/11878773_71

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“…Detection of other types of concepts in topics and image text has also been shown to improve retrieval performance [16][17][18] . One approach to visual retrieval has performed relatively well by making use of a great deal of machine learning 19 .…”

Section: Lessons Learnedmentioning

confidence: 99%

The ImageCLEFmed Medical Image Retrieval Task Test Collection

2008

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A growing number of clinicians, educators, researchers, and others use digital images in their work and search for them via image retrieval systems. Yet, this area of information retrieval is much less understood and developed than searching for text-based content, such as biomedical literature and its derivations. The goal of the ImageCLEF medical image retrieval task (ImageCLEFmed) is to improve understanding and system capability in search for medical images. In this paper, we describe the development and use of a medical image test collection designed to facilitate research with image retrieval systems and their users. We also provide baseline results with the new collection and describe them in the context of past research with portions of the collection.

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Section: Lessons Learnedmentioning

confidence: 99%