A Recommender System to Help Refining Clinical Research Studies

The process of refining the research question in a medical study depends greatly on the current background of the investigated subject. The information found in prior works can directly impact several stages of the study, namely the cohort definition stage. Besides previous published methods, researchers could also leverage on other materials, such as the output of cohort selection tools, to enrich and to accelerate their own work. However, this kind of information is not always captured by search engines. In this paper, we present a methodology, based on a combination of content-based retrieval and text annotation techniques, to identify relevant scientific publications related to a research question and to the selected data sources.

A Dynamic Programming Approach for Secure and Accurate Content-Based Retrieval of Medical Images (Preprint)

UNSTRUCTURED Recently, with the explosive growth in the number of available medical images generated by medical imaging systems, content-based retrieval of medical images has become an important method for the diagnosis and study of many diseases. Most existing methods find medical images similar to a given one based on the extraction and comparison of crucial image features. However, similarity values computed with low level visual features of an image generally do not match the similarity obtained from human observation well. The overall performance of these methods is thus often unsatisfactory. This paper proposes a dynamic programming approach for content-based retrieval of medical images. The approach represents an image with three different histograms that contain both crucial intensity and textural features of the image. The similarity between two images is evaluated with a dynamic programming approach that can optimally align the peaks in the corresponding histograms from both images. Experiments show that the proposed approach is able to generate retrieval results with high accuracy. A comparison with state-of-the-art approaches for content-based medical image retrieval shows that the proposed approach can achieve higher retrieval accuracy in the testing dataset. As a result, higher retrieval accuracy may lead to more reliable results for the diagnosis and treatment of many diseases. The proposed approach is thus potentially useful for improving the security of many applications in health informatics.

Practical Privacy-Preserving Content-Based Retrieval in Cloud Image Repositories

Capacity necessities for visual information have been expanding as of late, after the rise of numerous profoundly intelligent sight and sound administrations and applications for cell phones in both individual and corporate situations. This has been a key driving variable for the selection of cloud-based information re-appropriating arrangements. Nonetheless, re-appropriating information stockpiling to the Cloud additionally prompts new security challenges that must be painstakingly tended to, particularly with respect to protection. Right now propose a safe system for re-appropriated protection safeguarding capacity and recovery in huge shared picture vaults. Our proposition depends on IES-CBIR, a novel Picture Encryption Plan that displays Content-Based Picture Recovery properties. The system empowers scrambled stockpiling and looking through utilizing Content-Based Picture Recovery questions while safeguarding security against legitimate yet inquisitive cloud managers. We have fabricated a model of the proposed structure, officially broke down and demonstrated its security properties, and tentatively assessed its presentation and recovery exactness. Our outcomes show that IES-CBIR is provably secure, permits more productive tasks than existing proposition, both as far as reality multifaceted nature, and makes ready for new down to earth application situations.