X Chen, X Wang, K Zhang, KM Fung, TC Thai… - Medical image …, 2022 - Elsevier
Deep learning has received extensive research interest in developing 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 …
E Goceri - Artificial Intelligence Review, 2023 - Springer
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 …
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 …
A Kleppe, OJ Skrede, S De Raedt, K Liestøl… - Nature Reviews …, 2021 - nature.com
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 …
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 …