The remarkable performance of the Transformer architecture in natural language processing
has recently also triggered broad interest in Computer Vision. Among other merits …

Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …

Implicit neural representations (INRs) have emerged as a powerful paradigm in scene
reconstruction and computer graphics, showcasing remarkable results. By utilizing neural …

Transformers have recently gained attention in the computer vision domain due to their
ability to model long-range dependencies. However, the self-attention mechanism, which is …

Medical image segmentation has seen significant improvements with transformer models,
which excel in grasping far-reaching contexts and global contextual information. However …

Foundation models, large-scale, pre-trained deep-learning models adapted to a wide range
of downstream tasks have gained significant interest lately in various deep-learning …

Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …

While CNN-based methods have been the cornerstone of medical image segmentation due
to their promising performance and robustness, they suffer from limitations in capturing long …

Colorectal cancer is a prevalent disease in modern times, with most cases being caused by
polyps. Therefore, the segmentation of polyps has garnered significant attention in the field …

Abstract Vision Transformer (ViT) has emerged as a potential alternative to convolutional
neural networks for large datasets. However, applying ViT directly to medical image …