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

Recently, deep convolutional neural networks have achieved great success for medical
image segmentation. However, unlike segmentation of natural images, most medical images …

Although having achieved great success in medical image segmentation, deep learning-
based approaches usually require large amounts of well-annotated data, which can be …

Abstract 3D convolution neural networks (CNN) have been proved very successful in
parsing organs or tumours in 3D medical images, but it remains sophisticated and time …

While making a tremendous impact in various fields, deep neural networks usually require
large amounts of labeled data for training which are expensive to collect in many …

Deep learning algorithms, in particular 2D and 3D fully convolutional neural networks
(FCNs), have rapidly become the mainstream methodology for volumetric medical image …

Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in
extracting knowledge from unlabeled data to assist learning from labeled data. This paper …

The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical
images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent …

Learning visual features from unlabeled image data is an important yet challenging task,
which is often achieved by training a model on some annotation-free information. We …

There have been considerable debates over 2D and 3D representation learning on 3D
medical images. 2D approaches could benefit from large-scale 2D pretraining, whereas they …