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

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 …

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images.
Existing cell segmentation methods are often tailored to specific modalities or require …

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 …

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 …

Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) have been pivotal in
biomedical image segmentation, yet their ability to manage long-range dependencies …

Multiple myeloma disrupts normal blood cell production, requiring early detection due to the
increased risk of bone metastases. Although various artificial intelligence (AI) methods have …

Recently, transformers have been widely used in medical image segmentation to capture
long-range and global dependencies using self-attention. However, they often struggle to …

Medical image segmentation aims to locate lesions within a given image to assist doctors in
diagnosis and treatment, playing a crucial role in improving patient outcomes. Recently, the …

Background and objective: Cell segmentation in bright-field histological slides is a crucial
topic in medical image analysis. Having access to accurate segmentation allows …