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 …

Medical images are a rich source of invaluable necessary information used by clinicians.
Recent technologies have introduced many advancements for exploiting the most of this …

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 …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Segmentation of infections from CT scans is important for accurate diagnosis and follow-up
in tackling the COVID-19. Although the convolutional neural network has great potential to …

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …

Due to the intensive cost of labor and expertise in annotating 3D medical images at a voxel
level, most benchmark datasets are equipped with the annotations of only one type of …

For medical image segmentation, contrastive learning is the dominant practice to improve
the quality of visual representations by contrasting semantically similar and dissimilar pairs …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Annotating medical images, particularly for organ segmentation, is laborious and time-
consuming. For example, annotating an abdominal organ requires an estimated rate of 30 …