40,858 research outputs found
Asymmetric Co-Training with Explainable Cell Graph Ensembling for Histopathological Image Classification
Convolutional neural networks excel in histopathological image
classification, yet their pixel-level focus hampers explainability. Conversely,
emerging graph convolutional networks spotlight cell-level features and medical
implications. However, limited by their shallowness and suboptimal use of
high-dimensional pixel data, GCNs underperform in multi-class histopathological
image classification. To make full use of pixel-level and cell-level features
dynamically, we propose an asymmetric co-training framework combining a deep
graph convolutional network and a convolutional neural network for multi-class
histopathological image classification. To improve the explainability of the
entire framework by embedding morphological and topological distribution of
cells, we build a 14-layer deep graph convolutional network to handle cell
graph data. For the further utilization and dynamic interactions between
pixel-level and cell-level information, we also design a co-training strategy
to integrate the two asymmetric branches. Notably, we collect a private
clinically acquired dataset termed LUAD7C, including seven subtypes of lung
adenocarcinoma, which is rare and more challenging. We evaluated our approach
on the private LUAD7C and public colorectal cancer datasets, showcasing its
superior performance, explainability, and generalizability in multi-class
histopathological image classification
On Filter Size in Graph Convolutional Networks
Recently, many researchers have been focusing on the definition of neural
networks for graphs. The basic component for many of these approaches remains
the graph convolution idea proposed almost a decade ago. In this paper, we
extend this basic component, following an intuition derived from the well-known
convolutional filters over multi-dimensional tensors. In particular, we derive
a simple, efficient and effective way to introduce a hyper-parameter on graph
convolutions that influences the filter size, i.e. its receptive field over the
considered graph. We show with experimental results on real-world graph
datasets that the proposed graph convolutional filter improves the predictive
performance of Deep Graph Convolutional Networks.Comment: arXiv admin note: text overlap with arXiv:1811.0693
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
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