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

The loss function is an important component in deep learning-based segmentation methods.
Over the past five years, many loss functions have been proposed for various segmentation …

Medical image segmentation is a critical component in clinical practice, facilitating accurate
diagnosis, treatment planning, and disease monitoring. However, existing methods, often …

Training segmentation models for medical images continues to be challenging due to the
limited availability of data annotations. Segment Anything Model (SAM) is a foundation …

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 …

We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …

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 …

Machine learning techniques used in computer-aided medical image analysis usually suffer
from the domain shift problem caused by different distributions between source/reference …

Biomedical imaging is a driver of scientific discovery and a core component of medical care
and is being stimulated by the field of deep learning. While semantic segmentation …

As an emerging biomedical image processing technology, medical image segmentation has
made great contributions to sustainable medical care. Now it has become an important …