While deep convolutional neural networks (CNN) have been successfully applied to 2D
image analysis, it is still challenging to apply them to 3D medical images, especially when …

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 …

Imaging anisotropy poses a critical challenge in applying deep learning models to 3D
medical image analysis. Anisotropy downgrades model performance, especially when slice …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

Over the last decade, convolutional neural networks have emerged and advanced the state-
of-the-art in various image analysis and computer vision applications. The performance of …

Segmentation of 3D images is a fundamental problem in biomedical image analysis. Deep
learning (DL) approaches have achieved the state-of-the-art segmentation performance. To …

The convolutional neural network (CNN) has become a powerful tool for various biomedical
image analysis tasks, but there is a lack of visual explanation for the machinery of CNNs. In …

The performance on deep learning is significantly affected by volume of training data.
Models pre-trained from massive dataset such as ImageNet become a powerful weapon for …

Deep learning highly relies on the quantity of annotated data. However, the annotations for
3D volumetric medical data require experienced physicians to spend hours or even days for …

Witnessed the development of deep learning, increasing number of studies try to build
computer aided diagnosis systems for 3D volumetric medical data. However, as the …