Advancing Biomedical Image Retrieval: Development and Analysis of a Test Collection

Objective: Develop and analyze results from an image retrieval test collection. Methods: After participating research groups obtained and assessed results from their systems in the image retrieval task of Cross-Language Evaluation Forum, we assessed the results for common themes and trends. In addition to overall performance, results were analyzed on the basis of topic categories (those most amenable to visual, textual, or mixed approaches) and run categories (those employing queries entered by automated or manual means as well as those using visual, textual, or mixed indexing and retrieval methods). We also assessed results on the different topics and compared the impact of duplicate relevance judgments. Results: A total of 13 research groups participated. Analysis was limited to the best run submitted by each group in each run category. The best results were obtained by systems that combined visual and textual methods. There was substantial variation in performance across topics. Systems employing textual methods were more resilient to visually oriented topics than those using visual methods were to textually oriented topics. The primary performance measure of mean average precision (MAP) was not necessarily associated with other measures, including those possibly more pertinent to real users, such as precision at 10 or 30 images. Conclusions: We developed a test collection amenable to assessing visual and textual methods for image retrieval. Future work must focus on how varying topic and run types affect retrieval performance. Users' studies also are necessary to determine the best measures for evaluating the efficacy of image retrieval system

Relevance criteria for medical images applied by health care professionals : A grounded theory study.

This thesis studies relevance criteria for medical images' applied by health care professionals. The study also looks at the image information needs and image resources used by health care professionals, together with the image seeking behaviour of health care professionals from different disciplines. The work is a qualitative study that uses the Straussian version of grounded theory. The population of the study included health care professionals from different health and biomedical departments who worked in Sheffield Teaching Hospitals NHS Foundation Trust. In total twenty-nine health care professionals participated in this study and fifteen relevance criteria were identified from the data collected using semi-structured interviews and think-aloud protocols. The work forms part of the medical image retrieval track of ImageCLEF (ImageCLEFMed), and investigated the use of relevance criteria applied to search statements. Analysis indicates that some of the criteria identified by participants could be included in new topics used for future versions of the track. The findings of the study showed that health care professionals paid more attention to the visual attributes of medical images when selecting images and that they applied topical relevancy as the most frequent and most important criterion. The study found that health care professionals looked for medical images mainly for educational and research purposes and judged the relevancy of medical images based on their pictorial information needs and the image resources they used. We identified the difficulties that health care professionals faced when searching medical images in different image resources. Other findings also highlighted the need for, and the value of, looking at narrower subject communities within health and biomedical sciences for better understanding of relevance judgment and image seeking behaviour of the health care professionals

Geographic information extraction from texts

A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction