Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

A large-scale and well-annotated dataset is a key factor for the success of deep learning in
medical image analysis. However, assembling such large annotations is very challenging …

Feature vectors provided by pre-trained deep artificial neural networks have become a
dominant source for image representation in recent literature. Their contribution to the …

Advancements in computer vision and artificial intelligence (AI) carry the potential to make
significant contributions to health care, particularly in diagnostic specialties such as …

The increasing adoption of the whole slide image (WSI) technology in histopathology has
dramatically transformed pathologists' workflow and allowed the use of computer systems in …

Advances in high-throughput and multiplexed microfluidics have rewarded biotechnology
researchers with vast amounts of data but not necessarily the ability to analyze complex data …

Deep learning (DL) approaches to medical image analysis tasks have recently become
popular; however, they suffer from a lack of human interpretability critical for both increasing …

Accurate breast cancer detection using automated algorithms remains a problem within the
literature. Although a plethora of work has tried to address this issue, an exact solution is yet …

The prevalence of dementia and other neurodegenerative diseases is rapidly increasing in
aging nations. These relentless and progressive diseases remain largely without disease …