The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Objective There has been a large amount of research in the field of artificial intelligence (AI)
as applied to clinical radiology. However, these studies vary in design and quality and …

In the past decade, deep neural networks, particularly convolutional neural networks, have
revolutionised computer vision. However, all deep learning models may require a large …

The rapidly evolving field of sound classification has greatly benefited from the methods of
other domains. Today, the trend is to fuse domain-specific tasks and approaches together …

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic
burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to …

Image segmentation is an important task in many medical applications. Methods based on
convolutional neural networks attain state-of-the-art accuracy; however, they typically rely on …

Data augmentation is a popular technique which helps improve generalization capabilities
of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal …

Deep learning has revolutionized the detection of diseases and is helping the healthcare
sector break barriers in terms of accuracy and robustness to achieve efficient and robust …

In the recent pandemic, accurate and rapid testing of patients remained a critical task in the
diagnosis and control of COVID-19 disease spread in the healthcare industry. Because of …

Improvement of deep learning algorithms in smart agriculture is important to support the
early detection of plant diseases, thereby improving crop yields. Data acquisition for …