Deep convolutional neural networks (CNNs) have achieved commendable results on a
variety of medical image segmentation tasks. However, CNNs usually require a large …

Convolutional neural networks (CNNs) have made enormous progress in medical image
segmentation. The learning of CNNs is dependent on a large amount of training data with …

Abstract Background and Objective: Open-source deep learning toolkits are one of the
driving forces for developing medical image segmentation models that are essential for …

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

For medical image segmentation, most fully convolutional networks (FCNs) need strong
supervision through a large sample of high-quality dense segmentations, which is taxing in …

Image segmentation is a fundamental problem in biomedical image analysis. Recent
advances in deep learning have achieved promising results on many biomedical image …

Deep neural networks have achieved promising results in a breadth of medical image
segmentation tasks. Nevertheless, they require large training datasets with pixel-wise …

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 …

Despite the success of deep learning methods in medical image segmentation tasks, the
human-level performance relies on massive training data with high-quality annotations …

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 …