The ability to detect failures and anomalies are fundamental requirements for building
reliable systems for computer vision applications, especially safety-critical applications of …

Since the rise of deep learning (DL) in the mid-2010s, cardiac magnetic resonance (CMR)
image segmentation has achieved state-of-the-art performance. Despite achieving inter …

Despite the recent improvements in overall accuracy, deep learning systems still exhibit low
levels of robustness. Detecting possible failures is critical for a successful clinical integration …

Efficient and reliable medical image analysis is indispensable in modern healthcare
settings. The conventional approaches in diagnostics and evaluations from a mere picture …

Automatic segmentation of brain tumors has the potential to enable volumetric measures
and high-throughput analysis in the clinical setting. Reaching this potential seems almost …

Building retrofit is an important facet in the drive to reduce global greenhouse gas
emissions. However, delivering building retrofit at scale is a significant challenge, especially …

As deep learning continues to dominate all state-of-the-art computer vision tasks, it is
increasingly becoming an essential building block for robotic perception. This raises …

Many studies on machine learning (ML) for computer-aided diagnosis have so far been
mostly restricted to high-quality research data. Clinical data warehouses, gathering routine …

Multi-modal learning focuses on training models by equally combining multiple input data
modalities during the prediction process. However, this equal combination can be …

Deep learning methods have achieved an excellent performance in medical image
segmentation. However, the practical application of deep learning-based segmentation …