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 …

Standard 3D convolution operations usually require larger amounts of memory and
computation cost than 2D convolution operations. The fact increases the difficulty of the …

Recently, convolutional neural networks with 3D kernels (3D CNNs) have been very popular
in computer vision community as a result of their superior ability of extracting spatio-temporal …

Recent advances in 2D CNNs and vision transformers (ViTs) reveal that large kernels are
essential for enough receptive fields and high performance. Inspired by this literature, we …

In computer vision, 2D convolution is arguably the most important operation performed by a
ConvNet. Unsurprisingly, it has been the focus of intense software and hardware …

Most 3D neural networks are trained from scratch owing to the lack of large-scale labeled 3D
datasets. In this paper, we present a novel 3D pretraining method by leveraging 2D …

Relying on the rapidly increasing capacity of computing clusters and hardware,
convolutional neural networks (CNNs) have been successfully applied in various fields and …

Expert annotation of 3D medical image for downstream analysis is resource-intensive,
posing challenges in clinical applications. Visual self-supervised learning (vSSL), though …

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 …

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 …