We use a UNet [7] based encoder (e)-decoder (d) architecture. e consists of 6 convolutional
blocks, each consisting of two 3× 3 convolutions followed by a 2× 2 maxpooling layer with …

A key requirement for the success of supervised deep learning is a large labeled dataset-a
condition that is difficult to meet in medical image analysis. Self-supervised learning (SSL) …

Accurate image segmentation is important for many downstream clinical applications like
diagnosis, surgery planning. In recent years, deep neural networks have been quite …

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 …

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 …

Despite that deep learning has achieved state-of-the-art performance for medical image
segmentation, its success relies on a large set of manually annotated images for training that …

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 …

Medical image segmentation (MIS) plays an instrumental role in medical image analysis,
where considerable efforts have been devoted to automating the process. Currently …

Medical image segmentation plays an important role in many image-guided clinical
approaches. However, existing segmentation algorithms mostly rely on the availability of …