Object With the development of deep learning, the number of training samples for medical
image-based diagnosis and treatment models is increasing. Generative Adversarial …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Abstract Domain generalization is a ubiquitous challenge for machine learning in
healthcare. Model performance in real-world conditions might be lower than expected …

In medical imaging, the characteristics purely derived from a disease should reflect the
extent to which abnormal findings deviate from the normal features. Indeed, physicians often …

Deep neural networks are commonly used for medical purposes such as image generation,
segmentation, or classification. Besides this, they are often criticized as black boxes as their …

In recent years, deep learning (DL) has been recognized very useful in the semantic
segmentation of biomedical images. Such an application, however, is significantly hindered …

Conditional image generation plays a vital role in medical image analysis as it is effective in
tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models …

Every novel technology adds hidden vulnerabilities ready to be exploited by a growing
number of cyber-attacks. Automated software testing can be a promising solution to quickly …

Acquiring annotated data at scale with rare diseases or conditions remains a challenge. It
would be extremely useful to have a method that controllably synthesizes images that can …

ABSTRACT In deep learning, GANs (Generative Adversarial Networks) are one of the
prominent study areas due to their ability to generate synthetic data thereby solving the …