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

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

An increasing number of public datasets have shown a marked impact on automated organ
segmentation and tumor detection. However, due to the small size and partially labeled …

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Recently, human monkeypox outbreaks have been reported in many countries. According to
the reports and studies, quick determination and isolation of infected people are essential to …

Data-driven machine learning (ML) has emerged as a promising approach for building
accurate and robust statistical models from medical data, which is collected in huge volumes …

Artificial intelligence (AI) continues to garner substantial interest in medical imaging. The
potential applications are vast and include the entirety of the medical imaging life cycle from …

Labeled medical imaging data is scarce and expensive to generate. To achieve
generalizable deep learning models large amounts of data are needed. Standard data …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …