Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …

With the advent of the Internet of Things (IoT), artificial intelligence (AI), machine learning
(ML), and deep learning (DL) algorithms, the landscape of data-driven medical applications …

Abstract Fully Convolutional Neural Networks (FCNNs) with contracting and expanding
paths have shown prominence for the majority of medical image segmentation applications …

Since its renaissance, deep learning has been widely used in various medical imaging tasks
and has achieved remarkable success in many medical imaging applications, thereby …

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19
associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation …

The medical imaging literature has witnessed remarkable progress in high-performing
segmentation models based on convolutional neural networks. Despite the new …

Recent advances in deep learning for medical image segmentation demonstrate expert-
level accuracy. However, application of these models in clinically realistic environments can …

The performance on deep learning is significantly affected by volume of training data.
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for …

The recent outbreak of Coronavirus Disease 2019 (COVID-19) has led to urgent needs for
reliable diagnosis and management of SARS-CoV-2 infection. The current guideline is using …

Although having achieved great success in medical image segmentation, deep learning-
based approaches usually require large amounts of well-annotated data, which can be …