Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

In the past years, deep neural networks (DNN) have become popular in many disciplines
such as computer vision (CV), natural language processing (NLP), etc. The evolution of …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

Recent studies have found that medical images are vulnerable to adversarial attacks.
However, it is difficult to protect medical imaging systems from adversarial examples in that …

In the past years, Deep Neural Networks (DNNs) have become popular in many disciplines
such as Computer Vision (CV), and the evolution of hardware has helped researchers to …

The artificial intelligence (AI) systems used for diagnosing ophthalmic diseases have
significantly progressed in recent years. The diagnosis of difficult eye conditions, such as …

In the past years, deep neural networks (DNNs) have become popular in many disciplines
such as computer vision (CV). One of the most important challenges in the CV area is …

Deep learning techniques have achieved superior performance in computer-aided medical
image analysis, yet they are still vulnerable to imperceptible adversarial attacks, resulting in …

Deep neural networks have shown impressive performance for image-based disease
detection. Performance is commonly evaluated through clinical validation on independent …

Deep learning based methods for medical images can be easily compromised by
adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has …