Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

The medical field is creating large amount of data that physicians are unable to decipher
and use efficiently. Moreover, rule-based expert systems are inefficient in solving …

Researchers have started constructing systems that could automatically analyze the medical
images. In the initial phase (starting from 1970 to 1990), image processing was carried out …

Deep learning has shown promising results in medical image analysis, however, the lack of
very large annotated datasets confines its full potential. Although transfer learning with …

Purpose Segmentation of left ventricular myocardium (LVM) in coronary computed
tomography angiography (CCTA) is important for diagnosis of cardiovascular diseases. Due …

There is a pressing need in the medical imaging community to build large scale datasets
that are annotated with semantic descriptors. Given the cost of expert produced annotations …

Mechanisms are provided to implement a neural network, a concept extractor, and a
machine learning model that operate to provide automatic contour annotation of medical …

The need for time and attention, given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …

Background The automatic recognition of human body parts in three‐dimensional medical
images is important in many clinical applications. However, methods presented in prior …