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 …

Over the last decade, neural networks have reached almost every field of science and
become a crucial part of various real world applications. Due to the increasing spread …

Segmentation of curvilinear structures such as vasculature and road networks is challenging
due to relatively weak signals and complex geometry/topology. To facilitate and accelerate …

The interest in machine learning (ML) has grown tremendously in recent years, partly due to
the performance leap that occurred with new techniques of deep learning, convolutional …

The ability to explain decisions made by AI systems is highly sought after, especially in
domains where human lives are at stake such as medicine or autonomous vehicles. While it …

Clinical diagnosis and treatment decisions rely upon the integration of patient-specific data
with clinical reasoning. Cancer presents a unique context that influences treatment …

Leading neuroimaging studies have pushed 3T MRI acquisition resolutions below 1.0 mm
for improved structure definition and morphometry. Yet, only few, time-intensive automated …

Mission-critical applications that rely on deep learning (DL) for automation suffer because
DL models struggle to provide reliable indicators of failure. Reliable failure prediction can …

Official governmental monitoring of deforestation in the Brazilian Amazon relies on human
experts conducting visual analyzes of remote sensing images, an approach that is very …