T Sipola, T Kokkonen - Trends and Applications in Information Systems …, 2021 - Springer
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 …
MJ Tsai, PY Lin, ME Lee - Cancers, 2023 - mdpi.com
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 …
A Minagi, H Hirano, K Takemoto - Journal of Imaging, 2022 - mdpi.com
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 …
Y Xie, AE Fetit - Annual Conference on Medical Image Understanding …, 2022 - Springer
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 …
Machine learning (ML) is a rapidly developing area of medicine that uses significant resources to apply computer science and statistics to medical issues. ML's proponents laud …
S Kaviani, KJ Han, I Sohn - Expert Systems with Applications, 2022 - Elsevier
In recent years, medical images have significantly improved and facilitated diagnosis in versatile tasks including classification of lung diseases, detection of nodules, brain tumor …
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 …