Deep reinforcement learning (DRL) augments the reinforcement learning framework, which
learns a sequence of actions that maximizes the expected reward, with the representative …

Image-based navigation is widely considered the next frontier of minimally invasive surgery.
It is believed that image-based navigation will increase the access to reproducible, safe, and …

Artificial intelligence (AI) now enables automated interpretation of medical images. However,
AI's potential use for interventional image analysis remains largely untapped. This is …

Data-driven computational approaches have evolved to enable extraction of information
from medical images with reliability, accuracy, and speed, which is already transforming …

Cone-beam computed tomography (CBCT) imaging is becoming increasingly important for a
wide range of applications such as image-guided surgery, image-guided radiation therapy …

In this article, we perform a review of the state-of-the-art of hybrid machine learning in
medical imaging. We start with a short summary of the general developments of the past in …

Robot-guided computed tomography enables the inspection of parts that are too large for
conventional systems and allows, for instance, the non-destructive and volumetric …

Deep learning based approaches have been used to improve image quality in cone-beam
computed tomography (CBCT), a medical imaging technique often used in applications such …

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel
size, but are generally with sparse regions of interest, which leads to weak diagnostic …

The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …