In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …

In the field of medical image analysis within deep learning (DL), the importance of
employing advanced DL techniques cannot be overstated. DL has achieved impressive …

Abstract Research around Spiking Neural Networks has ignited during the last years due to
their advantages when compared to traditional neural networks, including their efficient …

Deep learning approaches have demonstrated great achievements in the field of computer-
aided medical image analysis, improving the precision of diagnosis across a range of …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

An important limitation of state-of-the-art deep learning networks is that they do not
recognize when their input is dissimilar to the data on which they were trained and proceed …

Implementing neural networks for clinical use in medical applications necessitates the ability
for the network to detect when input data differs significantly from the training data, with the …

Objectives: Machine learning (ML) is a powerful asset to support physicians in decision-
making procedures, providing timely answers. However, ML for health systems can suffer …

Out-of-distribution~(OOD) detection is the key to deploying models safely in the open world.
For OOD detection, collecting sufficient in-distribution~(ID) labeled data is usually more time …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …