… massive number of medical images and therefore uncovers … the history of AI in medical
imaging research, the current role, … Then, for a given testing image, the trained algorithm is …

… Thus, in the following, we describe the basic methodological aspects of two of the most
widely used algorithms (Random Forests and CNNs), as well as the increasingly popular GANs, …

… to simulate medical imaging by computational intelligence to … with the fusion algorithm can
simulate medical imaging. The … The M-Fusion and M-Update function by the fusion algorithm …

… medical imaging applications using these algorithms to diagnose the errors in disease diagnostic
systems which may result in extremely ambiguous medical … fields bring intelligence into …

… in popularity in the medical imaging of the liver. … algorithms and deep learning algorithms,
especially convolutional neural networks, and their clinical application in the medical imaging …

… image features should be valued to support decisions [4]. One of the key advantages of AI in
medical imaging … machine learning algorithms, AI systems can analyze medical images with …

… AI algorithms, there are many unique considerations when applying AI to medical imaging;
… current challenges related to AI research in medical imaging. The second portion will review …

… segmentation, thereby enhancing the performance of any algorithm that uses image
segmentation as a preprocessing step. For example, in electron microscopy, CNNs have been …

… Deep learning, which is a subset of AI algorithms, is widely used in medical imaging. The
idea of deep learning is designed in a way in which the model consists of many layers where …

… Although initial hype suggested that AI algorithms … of AI algorithms for medical imaging [8].
The translational portion of the workshop considered medical imaging use cases for algorithm …