Shangri–La : a medical case–based retrieval tool

García Seco de Herrera, Alba (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Schaer, Roger (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis))

Large amounts of medical visual data are produced in hospitals daily and made available continuously via publications in the scientific literature, representing the medical knowledge. However, it is not always easy to find the desired information and in clinical routine the time to fulfil an information need is often very limited. Infor-mation retrieval systems are a useful tool to provide access to these documents/images in the biomedical literature related to information needs of medical professionals. Shangri-La is a medical retrieval sys-tem that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when query-ing a case description and attached images. The system is based on a multi-modal retrieval approach with a focus on the integration of visual information connected to text. The approach includes a query-adaptive multi-modal fusion criterion that analyses if visual features are suitable to be fused with text for the retrieval. Furthermore, image modality information is integrated in the retrieval step. The approach is evaluated using the ImageCLEFmed 2013 medical retrieval bench-mark and can thus be compared to other approaches. Results show that the final approach outperforms the best multi-modal approach submitted to ImageCLEFmed 2013.


Keywords:
Article Type:
scientifique
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Economie/gestion
Date:
2017
Pagination:
34 p.
Published in:
Journal of the association for information science and technology
Numeration (vol. no.):
2017, vol. 68, no 11, pp. 2587–2601
DOI:
ISSN:
2330-1635
Appears in Collection:



 Record created 2017-10-28, last modified 2018-12-20

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