Generative adversarial networks (GANs) are unsupervised deep learning approach in the
computer vision community which has gained significant attention from the last few years in …

Data-driven approaches have great potential to shape future practices in radiology. The
most straightforward strategy to obtain clinically accurate models is to use large, well …

You are holding in your hands… oh, come on, who holds books like this in their hands
anymore? Anyway, you are reading this, and it means that I have managed to release one of …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

The wide prevalence of brain tumors in all age groups necessitates having the ability to
make an early and accurate identification of the tumor type and thus select the most …

Privacy concerns around sharing personally identifiable information are a major barrier to
data sharing in medical research. In many cases, researchers have no interest in a particular …

Chest radiography is a significant diagnostic tool used to detect diseases afflicting the chest.
The automatic detection techniques associated with computer vision are being adopted in …

Generative adversarial networks (GANs) have been recently applied to medical imaging on
different modalities (MRI, CT, X-ray, etc). However there are not many applications on …

The pulmonary nodules' malignancy rating is commonly confined in patient follow-up;
examining the nodule's activity is estimated with the Positron Emission Tomography (PET) …

The insufficiency of annotated medical imaging scans for cancer makes it challenging to
train and validate data-hungry deep learning models in precision oncology. We propose a …