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Re: [MM] use case

From: Jacco van Ossenbruggen <Jacco.van.Ossenbruggen@cwi.nl>
Date: Fri, 21 Oct 2005 09:23:25 +0200
Message-ID: <4358976D.1050004@cwi.nl>
To: Jane Hunter <jane@dstc.edu.au>, "'swbp'" <public-swbp-wg@w3.org>


Thanks for the use case.  I like the topic, but have two main comments.

- I think the description is a bit too broad, covering related issues 
from bioinformatics and image data mining.
I would like to stick to the issues that relate directly to image 
metadata and annotation, and phrase it a way that it describes a 
concrete problem for which current (SemWeb) technologies have (at least) 
a partial answer.
- We like to link to example solutions for each use case.  So I would 
like to have one or more medical images with associated RDF or OWL 
metadata, and a description about how and with what tools these 
annotation have been made, and what other software can be used to take 
advantage of the metadata. 
The goal is to provide technical insight ("how do the angle brackets 
look like")  but also some insight in what the costs are and what the 
pay off is.

Do you think you have concrete examples from one of your research 
projects that we can use to explain how SemWeb helps in solving this 
type of use cases?


Jane Hunter wrote:

>Will this do?
>Images constitute a primary data source in medicine. Images originate from
>diagnostic technologies (ultrasound, magnetic, fluorographic, radiological),
>surgical procedures, pathology (e.g. light and electron microscopy) and the
>research laboratory. Moving images are important in diagnostic radiology and
>cardiology, and are being investigated in the treatment of psychosis.
>Medical training relies increasingly on images, with the "visible human" and
>physiome projects as key examples. With the spread of telemedicine, images
>can be transmitted for remote collaborative interpretation.
>Medical image bioinformatics refers to the technologies, standards,
>protocols, algorithms, software, data structures, analyses and systems by
>which the rich information in an electronic image can be integrated into the
>broader knowledge fabric of molecular bioinformatics, including associated
>computational tools and database resources. The aim being to improve
>diagnostic systems for clinicians. Key issues and challenges in medical
>image bioinformatics include:
>   Automated storage, indexing, object recognition, segmentation, semantic
>labelling and markup of images. This requires automated workflows, and tools
>for fast, efficient metadata capture and generation, 3D reconstruction of
>images and representation of non-visual output from imagers, development of
>domain-specific ontologies, and generation of semantic annotations and
>inferencing rules.
>   Information integration and correlation. Because the pertinent data are
>too large and diverse for human correlation, knowledge management and mining
>services are required that can efficiently organise, discover, pre-process,
>correlate, reason-over and integrate medical imagery (X-ray, CT, MRI) with
>molecular sequence, structure, SNP, haplotype, expression microarray,
>pathway and other bioinformatic databases, medical and biomedical
>literature, and patient and other healthcare records.
>   Innovative presentation and visualisation interfaces to assist
>clinicians with decision-making. These must enable sophisticated search,
>browse and data exploration, fast similarity searches, statistical analysis,
>computational modelling, hypothesis formulation, and multi-modal
>visualisation over large data volumes.
Received on Friday, 21 October 2005 07:23:58 UTC

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