Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …

Abstract The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion
analysis is an emerging field of research that has the potential to alleviate the burden and …

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 …

Skin lesion segmentation from dermoscopic images plays a vital role in early diagnoses and
prognoses of various skin diseases. However, it is a challenging task due to the large …

Despite the success of deep neural networks in medical image classification, the problem
remains challenging as data annotation is time-consuming, and the class distribution is …

Recently, deep semi-supervised graph embedding learning has drawn much attention for its
appealing performance on the data with a pre-specified graph structure, which could be …

Automated colorectal cancer (CRC) segmentation in medical imaging is the key to achieving
automation of CRC detection, staging, and treatment response monitoring. Compared with …

Accurate tumor segmentation of tumor images can assist doctors to diagnose diseases.
However, achieving very high precision in tumor segmentation requires a large amount of …

The high precedence of epidemiological examination of skin lesions necessitated the well-
performing efficient classification and segmentation models. In the past two decades …

Objective. A semi-supervised learning method is an essential tool for applying medical
image segmentation. However, the existing semi-supervised learning methods rely heavily …