Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space,
perform particularly well in various tasks that process graph structure data. With the rapid …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Everything is connected and thus networks are instrumental in not only modeling complex
systems with many components, but also accommodating knowledge about their …

Graph convolutional network (GCN) has attracted much attention in the field of hyperspectral
image classification for its excellent feature representation and convolution on arbitrarily …

There have been successful applications of deep learning to functional magnetic resonance
imaging (fMRI), where fMRI data were mostly considered to be structured grids, and spatial …

Abstract Machine learning is playing an increasingly important role in medical image
analysis, spawning new advances in the clinical application of neuroimaging. There have …

Convolutional Neural Networks (CNNs) have achieved overwhelming success in learning-
related problems for 2D/3D images in the Euclidean space. However, unlike in the …

In pathology, tissue samples are assessed using multiple staining techniques to enhance
contrast in unique histologic features. In this paper, we introduce a multimodal CNN-GNN …

Deep learning approaches, especially convolutional neural networks (CNNs), have become
the method of choice in the field of medical image analysis over the last few years. This …

The parcellation of the human cortex into meaningful anatomical units is a common step of
various neuroimaging studies. There have been multiple successful efforts to process …