Closing the semantic gap in medical image analysis is critical. Access to large-scale
datasets might help to narrow the gap. However, large and balanced datasets may not …

Learning and extracting representative features along with similarity measurements in high
dimensional feature spaces is a critical task. Moreover, the problem of how to bridge the …

Advances in medical imaging technologies have led to the generation of large databases
with high-resolution image volumes. To retrieve images with pathology similar to the one …

In medical applications, retrieving similar images from repositories is most essential for
supporting diagnostic imaging-based clinical analysis and decision support systems …

Computer-based medical image retrieval (CBMIR) system helps practitioners to enhance
their diagnostic abilities, speeds up accurate diagnosis, and minimizes intra-and inter …

With a widespread use of digital imaging data in hospitals, the size of medical image
repositories is increasing rapidly. This causes difficulty in managing and querying these …

Approximately 12 percent of men will suffer from prostate cancer during their lifetime. While
some types of this cancer grow slowly and need minimal treatment, others are aggressive …

Content-based image retrieval is an effective and efficient technique to retrieve images from
a big dataset with similar images. To have a robust retrieval system, a proper and accurate …

Medical images can be a valuable resource for reliable information to support medical
diagnosis. However, the large volume of medical images makes it challenging to retrieve …

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 …