With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

As an emerging and challenging problem in the computer vision community, weakly
supervised object localization and detection plays an important role for developing new …

Abstract In recent years, Artificial Intelligence (AI) has proven its relevance for medical
decision support. However, the “black-box” nature of successful AI algorithms still holds back …

The development of deep learning approaches to detect, segment or classify structures of
interest has transformed the field of quantitative microscopy. High-throughput quantitative …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Advances in artificial intelligence (AI), especially deep learning (DL), have facilitated
magnetic resonance imaging (MRI) data analysis, enabling AI-assisted medical image …

Background and Objectives Perivascular spaces (PVS) are emerging markers of cerebral
small vessel disease (CSVD), but research on their determinants has been hampered by …

The feasibility of deep convolutional neural network for fabric defect detection has been
proven, but the detection performance often depends on large-scale labeled datasets …

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of
perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This …

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and ultrasound (US),
which are two common modalities for clinical breast tumor diagnosis besides Mammograms …