Shangri–La: A medical case–based retrieval tool

Abstract

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. Information 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 system that can potentially help clinicians to make decisions on difficult cases. It retrieves articles from the biomedical literature when querying 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 queryadaptive 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 benchmark and can thus be compared to other approaches. Results show that the final approach outperforms the best multi-modal approach

JASIST special issue on biomedical information retrieval

“…In a paper by Garcia Seco de Herrera et al (), titled “Shangri‐La: A Medical Case‐Based Retrieval Tool,” the authors deal with retrieval from visual data. The paper presents Shangri‐La, a medical retrieval system that retrieves articles from the biomedical literature when querying a case description and attached images, integrating visual information connected to text, and image modality information is integrated in the retrieval step.…”