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 …

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

Manual medical image segmentation is subjective and suffers from annotator-related bias,
which can be mimicked or amplified by deep learning methods. Recently, researchers have …

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 …

Manual annotation of medical images is highly subjective, leading to inevitable annotation
biases. Deep learning models may surpass human performance on a variety of tasks, but …

We propose SAMed, a general solution for medical image segmentation. Different from the
previous methods, SAMed is built upon the large-scale image segmentation model …

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 …