The role of physicians has always been to synthesize the data available to them to identify
diagnostic patterns that guide treatment and follow response. Today, increasingly …

Machine Learning is an increasingly important technology dealing with the growing
complexity of the digitalised world. Despite the fact, that we live in a 'Big data'world where …

Background Automated segmentation of anatomical structures is a crucial step in image
analysis. For lung segmentation in computed tomography, a variety of approaches exists …

We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …

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 …

Abstract Convolutional Neural Networks (CNNs) work very well for supervised learning
problems when the training dataset is representative of the variations expected to be …

Availability of high-quality large annotated data is indeed a significant challenge in
healthcare. In addition, privacy concerns and data-sharing restrictions often hinder access to …

Background Due to the advancement of medical imaging and computer technology,
machine intelligence to analyze clinical image data increases the probability of disease …

Artificial intelligence applications have shown success in different medical and health care
domains, and cardiac imaging is no exception. However, some machine learning models …

Deep convolutional neural networks (CNNs) have emerged as a new paradigm for
Mammogram diagnosis. Contemporary CNN-based computer-aided-diagnosis systems …