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 …

In the last decade, convolutional neural networks (ConvNets) have been a major focus of
research in medical image analysis. However, the performances of ConvNets may be limited …

Given that image registration is a fundamental and ubiquitous task in both clinical and
research domains of the medical field, errors in registration can have serious consequences …

Over the past decade, deep learning technologies have greatly advanced the field of
medical image registration. The initial developments, such as ResNet-based and U-Net …

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation
augments the surgeon's ability to achieve this but loses validity as surgery progresses due to …

The consideration of predictive uncertainty in medical imaging with deep learning is of
utmost importance. We apply estimation of both aleatoric and epistemic uncertainty by …

Uncertainty estimation in medical image registration enables surgeons to evaluate the
operative risk based on the trustworthiness of the registered image data thus of paramount …

In this paper we propose a supervised method to predict registration misalignment using
convolutional neural networks (CNNs). This task is casted to a classification problem with …

is an important problem in computer-assisted orthopedic surgery (CAOS). As one typical
example, the pre-operative space where the patient-specific surgical plan is usually made …

In order to tackle the difficulty associated with the ill-posed nature of the image registration
problem, regularization is often used to constrain the solution space. For most learning …