The artificial neural network (ANN), one of the machine learning (ML) algorithms, inspired by
the human brain system, was developed by connecting layers with artificial neurons …

Super-resolution (SR) reconstruction is a hot topic in medical image processing. SR implies
reconstructing corresponding high-resolution (HR) images from observed low-resolution …

Background Unsupervised learning can discover various unseen abnormalities, relying on
large-scale unannotated medical images of healthy subjects. Towards this, unsupervised …

Generative Adversarial Networks (GAN) have many potential medical imaging applications,
including data augmentation, domain adaptation, and model explanation. Due to the limited …

There is a growing demand for high-resolution (HR) medical images for both clinical and
research applications. Image quality is inevitably traded off with acquisition time, which in …

Abstract High-resolution (HR), isotropic cardiac Magnetic Resonance (MR) cine imaging is
challenging since it requires long acquisition and patient breath-hold times. Instead, 2D …

Robust machine learning models based on radiomic features might allow for accurate
diagnosis, prognosis, and medical decision-making. Unfortunately, the lack of standardized …

In biomedical image analysis, the applicability of deep learning methods is directly impacted
by the quantity of image data available. This is due to deep learning models requiring large …

In recent years, deep learning techniques have been applied in musculoskeletal radiology
to increase the diagnostic potential of acquired images. Generative adversarial networks …

Volumetric biomedical microscopy has the potential to increase the diagnostic information
extracted from clinical tissue specimens and improve the diagnostic accuracy of both human …