Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Deep neural networks (DNNs) have achieved state-of-the-art results in a broad range of
tasks, in particular the ones dealing with the perceptual data. However, full-scale application …

Sign languages play an essential role in the cognitive and social development of the deaf,
consisting of a natural form of communication and being a symbol of identity and culture …

Medical image segmentation is a complex yet one of the most essential tasks for diagnostic
procedures such as brain tumor detection. Several 3D Convolutional Neural Network (CNN) …

3D convolution is powerful for video classification but often computationally expensive,
recent studies mainly focus on decomposing it on spatial-temporal and/or channel …

The decomposition of 3D convolution will considerably reduce the computing complexity of
3D convolutional neural networks, yet simple stacking restricts the performance of neural …

Previous work utilized three-dimensional (3-D) convolutional neural networks (CNNs)
tomodel the spatial appearance and temporal evolution concurrently for sign language …

This paper proposes a method for visually explaining the decision-making process of 3D
convolutional neural networks (CNN) with a temporal extension of occlusion sensitivity …

Convolutional Neural Networks are the de facto models for image recognition. However 3D
CNNs, the straight forward extension of 2D CNNs for video recognition, have not achieved …

In this work we employ multitask learning to capitalize on the structure that exists in related
supervised tasks to train complex neural networks. It allows training a network for multiple …