The use of AI systems in healthcare for the early screening of diseases is of great clinical
importance. Deep learning has shown great promise in medical imaging, but the reliability …

Clinical trials consume the latter half of the 10 to 15 year, 1.5–2.0 billion USD, development
cycle for bringing a single new drug to market. Hence, a failed trial sinks not only the …

Fully convolutional neural networks (FCNs), and in particular U-Nets, have achieved state-of-
the-art results in semantic segmentation for numerous medical imaging applications …

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with
respect to the quantity of high-performing solutions reported in the literature. End users are …

In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence,
and it has been deployed in different fields of healthcare applications such as image …

Diagnosis and treatment guidance are aided by detecting relevant biomarkers in medical
images. Although supervised deep learning can perform accurate segmentation of …

Training robust deep learning (DL) systems for medical image classification or segmentation
is challenging due to limited images covering different disease types and severity. We …

The comprehensive integration of machine learning healthcare models within clinical
practice remains suboptimal, notwithstanding the proliferation of high-performing solutions …

Retinal optical coherence tomography (OCT) images provide fundamental information
regarding the health of the posterior eye (eg, the retina and choroid). Thus, the development …

In supervised learning for medical image analysis, sample selection methodologies are
fundamental to attain optimum system performance promptly and with minimal expert …