Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

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

Transformers have shown great promise in medical image segmentation due to their ability
to capture long-range dependencies through self-attention. However, they lack the ability to …

Transformers have recently lead to encouraging progress in computer vision. In this work,
we present new baselines by improving the original Pyramid Vision Transformer (PVT v1) by …

An efficient and effective decoding mechanism is crucial in medical image segmentation
especially in scenarios with limited computational resources. However these decoding …

Medical image analysis plays an important role in clinical diagnosis. In this paper, we
examine the recent Segment Anything Model (SAM) on medical images, and report both …

In this paper, we are the first to propose a new graph convolution-based decoder namely,
Cascaded Graph Convolutional Attention Decoder (G-CASCADE), for 2D medical image …

Automatic polyp segmentation can help physicians to effectively locate polyps (aka region of
interests) in clinical practice, in the way of screening colonoscopy images assisted by neural …

Recently, Meta AI Research releases a general Segment Anything Model (SAM), which has
demonstrated promising performance in several segmentation tasks. As we know, polyp …

Abstract We propose Dual Cross-Attention (DCA), a simple yet effective attention module
that enhances skip-connections in U-Net-based architectures for medical image …