Shortage of fully annotated datasets has been a limiting factor in develo** deep learning
based image segmentation algorithms and the problem becomes more pronounced in multi …

Federated learning holds great potential for enabling large-scale healthcare research and
collaboration across multiple centres while ensuring data privacy and security are not …

In modern Internet of Things (IoT), visual analysis and predictions are often performed by
deep learning models. Salient object detection (SOD) is a fundamental preprocessing for …

Quasi-static ultrasound elastography is an importance imaging technology to assess the
conditions of various diseases through reconstructing the tissue strain from radio frequency …

Magnetic Resonance Imaging (MRI) is a vital component of medical imaging. When
compared to other image modalities, it has advantages such as the absence of radiation …

Abstract Background Quantizing the Breast Imaging Reporting and Data System (BI-RADS)
criteria into different categories with the single ultrasound modality has always been a …

Vessel contour detection (VCD) in intravascular images is important for the quantitative
assessment of vessels. However, it is still a challenging task due to a high degree of …

The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh
challenges over the past two years. During this COVID-19 pandemic, there has been a need …

The recent big advancements in Generative Adversarial Networks (GANs) have opened
countless opportunities for synthetic data generation in many areas, including science …

Nasopharyngeal Carcinoma segmentation in magnetic resonance imagery (MRI) is vital to
radiotherapy. Exact dose delivery hinges on an accurate delineation of the gross tumor …