Indexing Medical Images based on Collaborative Experts Reports
This addresses the need for timely and reliable medical image analysis for patients and physicians, though it appears incremental in applying existing text extraction techniques to a new domain.
The paper tackles the problem of medical malpractice and physician unavailability by proposing a social network for collaborative diagnosis of medical images, resulting in a method to automatically extract keywords from expert comments for image indexing.
A patient is often willing to quickly get, from his physician, reliable analysis and concise explanation according to provided linked medical images. The fact of making choices individually by the patient's physician may lead to malpractices and consequently generates unforeseeable damages. The Institute of Medicine of the National Sciences Academy(IMNAS) in USA published a study estimating that up to 98,000 hospital deathseach year can be attributed to medical malpractice [1]. Moreover, physician, in charge of medical image analysis, might be unavailable at the right time, which may complicate the patient's state. The goal of this paper is to provide to physicians and patients, a social network that permits to foster cooperation and to overcome the problem of unavailability of doctors on site any time. Therefore, patients can submit their medical images to be diagnosed and commented by several experts instantly. Consequently, the need to process opinions and to extract information automatically from the proposed social network became a necessity due to the huge number of comments expressing specialist's reviews. For this reason, we propose a kind of comments' summary keywords-based method which extracts the major current terms and relevant words existing on physicians' annotations. The extracted keywords will present a new and robust method for image indexation. In fact, significant extracted terms will be used later to index images in order to facilitate their discovery for any appropriate use. To overcome this challenge, we propose our Terminology Extraction of Annotation (TEA) mixed approach which focuses on algorithms mainly based on statistical methods and on external semantic resources.