In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

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 …

Published research results are difficult to replicate due to the lack of a standard evaluation
data set in the area of decision support systems in mammography; most computer-aided …

This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …

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 …

The emergence of digital pathology has opened new horizons for histopathology. Artificial
intelligence (AI) algorithms are able to operate on digitized slides to assist pathologists with …

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 …

We present the application of different nonlinear image deformation models to the task of
image recognition. The deformation models are especially suited for local changes as they …

In this study we present an efficient image categorization and retrieval system applied to
medical image databases, in particular large radiograph archives. The methodology is …

This chapter gives an introduction to the methods of biomedical image processing. After
some fundamental preliminary remarks to the terminology used, medical imaging modalities …