Artificial intelligence (AI) with deep learning models has been widely applied in numerous
domains, including medical imaging and healthcare tasks. In the medical field, any judgment …

With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

In recent years, the growing number of medical imaging studies is placing an ever-
increasing burden on radiologists. Deep learning provides a promising solution for …

We propose a novel attention gate (AG) model for medical image analysis that automatically
learns to focus on target structures of varying shapes and sizes. Models trained with AGs …

The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …

Chest X-rays are one of the most common radiological examinations in daily clinical
routines. Reporting thorax diseases using chest X-rays is often an entry-level task for …

Synthesized medical images have several important applications, eg, as an intermedium in
cross-modality image registration and as supplementary training samples to boost the …

Computerized microscopy image analysis plays an important role in computer aided
diagnosis and prognosis. Machine learning techniques have powered many aspects of …

In this paper, we propose novel generative models for creating adversarial examples,
slightly perturbed images resembling natural images but maliciously crafted to fool pre …