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

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 …

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

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

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

Pixel-wise segmentation is one of the most data and annotation hungry tasks in our field.
Providing representative and accurate annotations is often mission-critical especially for …

Recent trends in semi-supervised learning have significantly boosted the performance of 3D
semi-supervised medical image segmentation. Compared with 2D images, 3D medical …

Deep learning has been applied successfully to many biomedical image segmentation
tasks. However, due to the diversity and complexity of biomedical image data, manual …

While deep learning models have become the predominant method for medical image
segmentation, they are typically not capable of generalizing to unseen segmentation tasks …