Deep Neural Networks in Medical Imaging

Dear Colleagues,

Medical Imaging plays a key role in disease management, starting from baseline risk assessment and through diagnosis, staging, therapy planning, therapy delivery, and follow-up. Each type of disease has led to the development of more advanced imaging methods and modalities to help clinicians address the specific challenges in analyzing the underlying mechanisms of diseases. Imaging data is one of the most important sources of evidence for clinical analysis and medical intervention as it accounts for about 90% of all healthcare data. Researchers have been actively pursuing the development of advanced image analysis algorithms, some of which are routinely used in clinical practice. These developments were driven by the need for a comprehensive quantification of structure and function across several imaging modalities such as Computed Tomography (CT), X-ray Radiography, Magnetic Resonance Imaging (MRI), Ultrasound, Nuclear Medicine Imaging, and Digital Pathology.

In the context of the availability of unprecedented data storage capacity and computational power, deep learning has become the state-of-the-art machine learning technique, providing unprecedented performance at learning patterns in medical images and great promise for helping physicians during clinical decision-making processes. Previously reported deep learning-related studies cover various types of problems (e.g., classification, detection, and segmentation) for different types of structures (e.g., landmarks, lesions, organs) in diverse anatomical application areas.

The aim of this Special Issue of Applied Sciences is to present and highlight novel methods, architectures, techniques, and applications of deep learning in medical imaging.

Prof. Dr. Lucian Mihai Itu
Prof. Dr. Constantin Suciu
Dr. Anamaria Vizitiu
Guest Editors

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