In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …

Ultrasound (US) imaging for medical purposes has been increasing in popularity over the
years. The US technology has some valuable strengths, such as it is harmless, very cheap …

Medical image segmentation remains particularly challenging for complex and low-contrast
anatomical structures. In this paper, we introduce the U-Transformer network, which …

Colonoscopy is considered the gold standard for detection of colorectal cancer and its
precursors. Existing examination methods are, however, hampered by high overall miss …

Even though convolutional neural networks (CNNs) are driving progress in medical image
segmentation, standard models still have some drawbacks. First, the use of multi-scale …

Accurate medical image segmentation is essential for diagnosis and treatment planning of
diseases. Convolutional Neural Networks (CNNs) have achieved state-of-the-art …

Segmentation of abdominal organs has been a comprehensive, yet unresolved, research
field for many years. In the last decade, intensive developments in deep learning (DL) …

In medical image analysis, it is typical to collect multiple annotations, each from a different
clinical expert or rater, in the expectation that possible diagnostic errors could be mitigated …

Automatic breast lesion segmentation in ultrasound helps to diagnose breast cancer, which
is one of the dreadful diseases that affect women globally. Segmenting breast regions …

Traditional convolutional neural networks have a limited receptive field while transformer-
based networks are mediocre in constructing long-term dependency from the perspective of …