Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

Medical image synthesis represents a critical area of research in clinical decision-making,
aiming to overcome the challenges associated with acquiring multiple image modalities for …

Generative adversarial models with convolutional neural network (CNN) backbones have
recently been established as state-of-the-art in numerous medical image synthesis tasks …

Lesion detectors based on deep learning can assist doctors in diagnosing diseases.
However, the performance of current detectors is likely to be unsatisfactory due to the …

Simple Summary The two most common cross-sectional imaging modalities, computed
tomography (CT) and magnetic resonance imaging (MRI), have shown enormous utility in …

Diabetic retinopathy (DR) is a complication of diabetes that severely affects eyes. It can be
graded into five levels of severity according to international protocol. However, optimizing a …

The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

Diabetic Retinopathy (DR) is a serious consequence of diabetes that seriously impact on the
eyes and is a leading cause of blindness. If the lesions in DR arise in the central portion of …

Stroke is a kind of cerebrovascular disease that heavily damages people's life and health.
The quantitative analysis of brain MRI images plays an important role in the diagnosis and …