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 …

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 …

With the rapid advancements in artificial intelligence, particularly in machine learning and
deep learning, automated disease diagnosis is becoming increasingly feasible. Generating …

The segmentation results of retinal blood vessels are crucial for automatically diagnosing
ophthalmic diseases such as diabetic retinopathy, hypertension, cardiovascular and …

Convolutional neural networks (CNN), especially numerous U-shaped models, have
achieved great progress in retinal vessel segmentation. However, a great quantity of global …

In image segmentation, effective upsampling plays a pivotal role in recovering lost spatial
information during the process of downsampling. Standard skip connections designed to …

Medical image segmentation involves identifying and separating object instances in a
medical image to delineate various tissues and structures, a task complicated by the …

The Transformer-based methods are still unable to effectively model local contexts although
they make up for the deficiency of remote information dependencies for approaches based …

Automatically and accurately segmenting skin lesions can be challenging, due to factors
such as low contrast and fuzzy boundaries. This paper proposes a hybrid encoder-decoder …

Vascular segmentation extracts blood vessels from images and serves as the basis for
diagnosing various diseases, like ophthalmic diseases. Ophthalmologists often require high …