Content-based image retrieval (CBIR) technology benefits not only large image collections management, but also helps clinical care, biomedical research, and education. Digital images are found in X-Rays, MRI, CT which are used for diagnosing and planning treatment schedules. Thus, visual information management is challenging as the data quantity available is huge. Currently, available medical d...

We live in a world where digital images are constantly generated during our daily activities, whether private or business. They play an important role life, showing moments, people, places, events and keeping their memory. Images unavoidable business, especially marketing, web sales, social networks, medicine, security, education. In general, contribute to better understanding of the message, i...

Deep hashing methods have been shown to be the most efficient approximate nearest neighbor search techniques for large-scale image retrieval. However, existing deep a poor small-sample ranking performance case-based medical The top-ranked images in returned query results may as different class than image. This problem is caused by classification, regions of interest (ROI), and information loss ...

This paper describes the participation of the SNUMedinfo team at the two retrieval tasks (Ad-hoc image-based retrieval and Case-based retrieval) in the ImageCLEF 2013 medical task. For the ad-hoc image-based retrieval task, we submitted 1 baseline textual run using query likelihood model in Indri search engine, and 4 visual runs utilizing various image features implemented in Lire image retriev...

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review sta...