Randomised controlled trials, including the National Lung Screening Trial (NLST) and the
NELSON trial, have shown reduced mortality with lung cancer screening with low-dose CT …

Deep learning has received extensive research interest in develo** new medical image
processing algorithms, and deep learning based models have been remarkably successful …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

Abstract Vision Transformers (ViT) s have shown great performance in self-supervised
learning of global and local representations that can be transferred to downstream …

Designing deep learning based methods with medical images has always been an attractive
area of research to assist clinicians in rapid examination and accurate diagnosis. Those …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer
death in the United States. Lung cancer screening using low-dose computed tomography …

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 …