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 …

Abstract Transformers are Deep Neural Networks (DNN) that utilize a self-attention
mechanism to capture contextual relationships within sequential data. Unlike traditional …

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 …

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 …

Brain tumor segmentation in multimodal MRI has great significance in clinical diagnosis and
treatment. The utilization of multimodal information plays a crucial role in brain tumor …

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 …

In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …