969 research outputs found
Geometric deep learning: going beyond Euclidean data
Many scientific fields study data with an underlying structure that is a
non-Euclidean space. Some examples include social networks in computational
social sciences, sensor networks in communications, functional networks in
brain imaging, regulatory networks in genetics, and meshed surfaces in computer
graphics. In many applications, such geometric data are large and complex (in
the case of social networks, on the scale of billions), and are natural targets
for machine learning techniques. In particular, we would like to use deep
neural networks, which have recently proven to be powerful tools for a broad
range of problems from computer vision, natural language processing, and audio
analysis. However, these tools have been most successful on data with an
underlying Euclidean or grid-like structure, and in cases where the invariances
of these structures are built into networks used to model them. Geometric deep
learning is an umbrella term for emerging techniques attempting to generalize
(structured) deep neural models to non-Euclidean domains such as graphs and
manifolds. The purpose of this paper is to overview different examples of
geometric deep learning problems and present available solutions, key
difficulties, applications, and future research directions in this nascent
field
Edge-aware Hard Clustering Graph Pooling for Brain Imaging Data
Graph Convolutional Networks (GCNs) can capture non-Euclidean spatial
dependence between different brain regions, and the graph pooling operator in
GCNs is key to enhancing the representation learning capability and acquiring
abnormal brain maps. However, the majority of existing research designs graph
pooling operators only from the perspective of nodes while disregarding the
original edge features, in a way that not only confines graph pooling
application scenarios, but also diminishes its ability to capture critical
substructures. In this study, a clustering graph pooling method that first
supports multidimensional edge features, called Edge-aware hard clustering
graph pooling (EHCPool), is developed. EHCPool proposes the first
'Edge-to-node' score evaluation criterion based on edge features to assess node
feature significance. To more effectively capture the critical subgraphs, a
novel Iteration n-top strategy is further designed to adaptively learn sparse
hard clustering assignments for graphs. Subsequently, an innovative N-E
Aggregation strategy is presented to aggregate node and edge feature
information in each independent subgraph. The proposed model was evaluated on
multi-site brain imaging public datasets and yielded state-of-the-art
performance. We believe this method is the first deep learning tool with the
potential to probe different types of abnormal functional brain networks from
data-driven perspective. Core code is at: https://github.com/swfen/EHCPool
Geometric deep learning
The goal of these course notes is to describe the main mathematical ideas behind geometric deep learning and to provide implementation details for several applications in shape analysis and synthesis, computer vision and computer graphics. The text in the course materials is primarily based on previously published work. With these notes we gather and provide a clear picture of the key concepts and techniques that fall under the umbrella of geometric deep learning, and illustrate the applications they enable. We also aim to provide practical implementation details for the methods presented in these works, as well as suggest further readings and extensions of these ideas
Interpretation of Brain Morphology in Association to Alzheimer's Disease Dementia Classification Using Graph Convolutional Networks on Triangulated Meshes
We propose a mesh-based technique to aid in the classification of Alzheimer's
disease dementia (ADD) using mesh representations of the cortex and subcortical
structures. Deep learning methods for classification tasks that utilize
structural neuroimaging often require extensive learning parameters to
optimize. Frequently, these approaches for automated medical diagnosis also
lack visual interpretability for areas in the brain involved in making a
diagnosis. This work: (a) analyzes brain shape using surface information of the
cortex and subcortical structures, (b) proposes a residual learning framework
for state-of-the-art graph convolutional networks which offer a significant
reduction in learnable parameters, and (c) offers visual interpretability of
the network via class-specific gradient information that localizes important
regions of interest in our inputs. With our proposed method leveraging the use
of cortical and subcortical surface information, we outperform other machine
learning methods with a 96.35% testing accuracy for the ADD vs. healthy control
problem. We confirm the validity of our model by observing its performance in a
25-trial Monte Carlo cross-validation. The generated visualization maps in our
study show correspondences with current knowledge regarding the structural
localization of pathological changes in the brain associated to dementia of the
Alzheimer's type.Comment: Accepted for the Shape in Medical Imaging (ShapeMI) workshop at
MICCAI International Conference 202
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