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 …

Computer-aided medical image analysis plays a significant role in assisting medical
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …

This chapter focuses on modern deep learning techniques that are proposed for
automatically recognizing and segmenting multiple organ regions on three-dimensional …

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 …

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 …

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 …

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 …

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 …

Three-dimensional convolutional neural networks (3D CNN) of artificial intelligence (AI) are
potent in image processing and recognition using deep learning to perform generative and …

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many
medical image analysis tasks. While trivial for humans, it is complex for automatic methods …