In recent years, deep learning (DL) methods have become powerful tools for biomedical
image segmentation. However, high annotation efforts and costs are commonly needed to …

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 …

Background With the development of deep learning (DL), more and more methods based on
deep learning are proposed and achieve state-of-the-art performance in biomedical image …

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

Automatic medical image segmentation plays a critical role in scientific research and
medical care. Existing high-performance deep learning methods typically rely on large …

Image segmentation is critical to lots of medical applications. While deep learning (DL)
methods continue to improve performance for many medical image segmentation tasks, data …

Smoothing hard-label assignments has emerged as a popular strategy in training
discriminative models. Nevertheless, most existing approaches are typically designed for …

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

Biomedical image segmentation plays a critical role in clinical diagnosis and medical
intervention. Recently, a variety of deep neural networks have boosted the biomedical …

The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …