Advancements in deep learning and computer vision provide promising solutions for
medical image analysis, potentially improving healthcare and patient outcomes. However …

The scarcity of high-quality annotated medical imaging datasets is a major problem that
collides with machine learning applications in the field of medical imaging analysis and …

The renaissance of deep learning has provided promising solutions to various tasks. While
conventional deep learning models are constructed for a single specific task, multi-task deep …

The collection and curation of large-scale medical datasets from multiple institutions is
essential for training accurate deep learning models, but privacy concerns often hinder data …

Deep learning has achieved great success in learning features from massive remote
sensing images (RSIs). To better understand the connection between three feature learning …

Deep learning evolves into a new form of machine learning technology that is classified
under artificial intelligence (AI), which has substantial potential for large-scale healthcare …

Over the past few years, the rapid development of deep learning technologies for computer
vision has significantly improved the performance of medical image segmentation …

Over the last decade, supervised deep learning on manually annotated big data has been
progressing significantly on computer vision tasks. But, the application of deep learning in …

Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …

Large-scale volumetric medical images with annotation are rare, costly, and time prohibitive
to acquire. Self-supervised learning (SSL) offers a promising pre-training and feature …