In the field of medical imaging, artificial intelligence solutions are used for diagnosis,
prediction and treatment processes. Such solutions are vulnerable to cyberattacks …

This paper explores the applicability of one-pixel attacks against medical imaging.
Successful attacks are threats that could cause mistrust towards artificial intelligence …

Recently, there has been increased interest in applying computer vision methodologies in
medical imaging, mainly due to the outstanding performance of deep learning. However …

Adversarial attacks are considered a potentially serious security threat for machine learning
systems. Medical image analysis (MedIA) systems have recently been argued to be …

Simple Summary As we increasingly rely on advanced imaging for medical diagnosis, it's
vital that our computer programs can accurately interpret these images. Even a single …

Transfer learning from natural images is used in deep neural networks (DNNs) for medical
image classification to achieve a computer-aided clinical diagnosis. Although the …

Adversarial attacks are carefully crafted inputs that can deceive machine learning models
into giving wrong results with seemingly high confidence. One approach that is commonly …

Deep neural networks (DNNs) have become popular for medical image analysis tasks like
cancer diagnosis and lesion detection. However, a recent study demonstrates that medical …

In recent years, medical images have significantly improved and facilitated diagnosis in
versatile tasks including classification of lung diseases, detection of nodules, brain tumor …

Abstract Background Deep neural networks (DNNs) are widely investigated in medical
image classification to achieve automated support for clinical diagnosis. It is necessary to …