Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of
mortality globally. At early stages, CVDs appear with minor symptoms and progressively get …

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure
considerably larger than the size of any research dataset. Therefore, the ability to analyze …

Curating a large set of fully annotated training data can be costly, especially for the tasks of
medical image segmentation. Scribble, a weaker form of annotation, is more obtainable in …

Deep learning has an enormous impact on medical image analysis. Many computer-aided
diagnostic systems equipped with deep networks are rapidly reducing human intervention in …

Large, fine-grained image segmentation datasets, annotated at pixel-level, are difficult to
obtain, particularly in medical imaging, where annotations also require expert knowledge …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Power quality issues are required to be addressed properly in forthcoming era of smart
meters, smart grids and increase in renewable energy integration. In this paper, Deep Auto …

Medical image segmentation plays a critical role in clinical decision-making, treatment
planning, and disease monitoring. However, accurate segmentation of medical images is …

Most recent scribble-supervised segmentation methods commonly adopt a CNN framework
with an encoder-decoder architecture. Despite its multiple benefits, this framework generally …