Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

Today, deep convolutional neural networks (CNNs) have demonstrated state of the art
performance for supervised medical image segmentation, across various imaging modalities …

Deep convolutional neural networks (CNN) proved to be highly accurate to perform
anatomical segmentation of medical images. However, some of the most popular CNN …

Deep convolutional networks recently made many breakthroughs in medical image
segmentation. Still, some anatomical artefacts may be observed in the segmentation results …

Medical image segmentation is a fundamental task for medical image analysis and surgical
planning. In recent years, UNet-based networks have prevailed in the field of medical image …

Despite the remarkable performance of deep learning methods on various tasks, most
cutting-edge models rely heavily on large-scale annotated training examples, which are …

Besides network architecture, researchers have recently focused their attention on the loss
function for the Convolutional Neural Network-based medical image segmentation. The loss …

Accurate medical image segmentation plays a crucial role in computer-assisted diagnosis
and monitoring. However, due to the complexity of medical images and the limitations of …

Deep learning has successfully been leveraged for medical image segmentation. It employs
convolutional neural networks (CNN) to learn distinctive image features from a defined pixel …

Medical image segmentation aims to identify and locate abnormal structures in medical
images, such as chest radiographs, using deep neural networks. These networks require a …