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

Automatic medical image segmentation is a crucial topic in the medical domain and
successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the …

The Transformer architecture has demonstrated remarkable results in 3D medical image
segmentation due to its capability of modeling global relationships. However, it poses a …

The Segment Anything Model (SAM) has recently gained popularity in the field of image
segmentation due to its impressive capabilities in various segmentation tasks and its prompt …

Purpose To present a deep learning segmentation model that can automatically and
robustly segment all major anatomic structures on body CT images. Materials and Methods …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

Medical image segmentation is crucial for healthcare, yet convolution-based methods like U-
Net face limitations in modeling long-range dependencies. To address this, Transformers …

Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

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

Transformer, the model of choice for natural language processing, has drawn scant attention
from the medical imaging community. Given the ability to exploit long-term dependencies …