Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

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 …

Synthetic data in machine learning for medicine and healthcare | Nature Biomedical Engineering
Skip to main content Thank you for visiting nature.com. You are using a browser version with …

Multimodal models trained on large natural image-text pair datasets have exhibited
astounding abilities in generating high-quality images. Medical imaging data is …

Generative models, such as DALL-E 2 (OpenAI), could represent promising future tools for
image generation, augmentation, and manipulation for artificial intelligence research in …

Deep learning techniques have promoted the rise of artificial intelligence (AI) and performed
well in computer vision. Medical image analysis is an important application of deep learning …

Radiography imaging protocols focus on particular body regions, therefore producing
images of great similarity and yielding recurrent anatomical structures across patients. To …

Chest X‐ray (CXR) imaging is one of the most widely used and economical tests to
diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge …

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 …

Thyroid nodules are one of the most common nodular lesions. The incidence of thyroid
cancer has increased rapidly in the past three decades and is one of the cancers with the …