With the exponential growth of medical image big data represented by high-resolution CT
images (CTI), the high-resolution CTI data is of great importance for clinical research and …

We explore the problem of classification within a medical image data-set based on a feature
vector extracted from the deepest layer of pre-trained Convolution Neural Networks. We …

Content-based medical image retrieval (CBMIR) is one of the most challenging and
ambiguous tasks used to minimize the semantic gap between images and human queries in …

Despite the progress made in the field of medical imaging, it remains a large area of open
research, especially due to the variety of imaging modalities and disease-specific …

In recent years, with extensive application in image retrieval and other tasks, a convolutional
neural network (CNN) has achieved outstanding performance. In this paper, a new content …

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 …

A convolutional neural network has the capacity to learn multiple representation levels and
abstraction in order to provide a better understanding of image data. In addition, a good …

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 …

Imitation learning is an efficient paradigm for teaching and controlling intelligent
autonomous cars. Obtaining a set of suitable demonstrations to learn an end-to-end policy …

Coronary artery disease (CAD) is the most common cardiovascular condition. It often leads
to a heart attack causing millions of deaths worldwide. Its accurate prediction using data …