In recent years, medical images have significantly improved and facilitated diagnosis in
versatile tasks including classification of lung diseases, detection of nodules, brain tumor …

Medical image analysis is currently experiencing a paradigm shift due to deep learning. This
technology has recently attracted so much interest of the Medical Imaging Community that it …

Medical image segmentation-the prerequisite of numerous clinical needs-has been
significantly prospered by recent advances in convolutional neural networks (CNNs) …

Recently, the vision transformer has achieved great success by pushing the state-of-the-art
of various vision tasks. One of the most challenging problems in the vision transformer is that …

Accurate and automatic segmentation of medical images is a crucial step for clinical
diagnosis and analysis. The convolutional neural network (CNN) approaches based on the …

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

Cancer is one of the leading causes of deaths in the last two decades. It is either diagnosed
malignant or benign–depending upon the severity of the infection and the current stage. The …

Automated skin lesion segmentation and classification are two most essential and related
tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep …

Melanoma skin cancer is considered as one of the most common diseases in the world.
Detecting such diseases at early stage is important to saving lives. During medical …

Skin cancer is a major public health problem that could benefit from computer-aided
diagnosis to reduce the burden of this common disease. Skin lesion segmentation from …