Set Representation Learning: A Framework for Learning Gigapixel Images
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Date
2021-09-13
Authors
Adnan, Mohammed
Advisor
Tizhoosh, Hamid
Journal Title
Journal ISSN
Volume Title
Publisher
University of Waterloo
Abstract
In Machine Learning, we often encounter data as a set of instances such Point Clouds (set of x,y, and z coordinates), patches from gigapixel images (Digital Pathology, Satellite Imagery, Astronomical Images, etc.), Weakly Supervised Learning, Multiple Instance Learning, and so on. It is then convenient to have Machine Learning or AI algorithms that can learn set representation. However, most of the progress made in the last two decades has been limited to single instance-based algorithms and smaller image datasets such as MNIST, CIFAR10, and CIFAR100. In this work, I present novel algorithms for Set Representation Learning. The contribution of this work is two-fold:
1. This work introduces three novel methods for learning Set Representations; Memory
based Exchangeable model (MEM), Graph Neural Network based Set Representation
Learning method, and a Hierarchical Set Representation Learning method.
2. This work demonstrates that learning gigapixel images can be formulated as a set
representation problem and provides a framework for efficiently learning gigapixel
image representations.
Different themes are explored for Set Representation Learning. This work investigates
Permutation Invariant Representations for Set Learning and introduces a new Permutation
Invariant method - ‘MEM’. Memory-based Exchangeable (MEM) model uses a Permutation
Invariant architecture and memory networks to learn inter-dependencies/relation between
different elements of the set. Subsequently, Graph Neural Networks (GNNs) are studied for
Set Representation Learning, and a new GNN based Set Representation Learning method
is proposed. Motivated by learning inter-dependencies among different elements in MEM,
the proposed method learns an equivalent graphical representation to model interaction
and interdependencies among different elements of the set. Lastly, this work introduces a
new learning scheme for learning Hierarchical Set Representations.
To demonstrate the efficacy of the proposed algorithms, they are validated and benchmarked on a variety of synthetic and real-world datasets such as MNIST, Point Clouds,
and Gaussian Distributions. Histopathology Images are used to demonstrate the application of Set Representation Learning for learning gigapixel images. State-of-the-art results
on all datasets are achieved, thus demonstrating efficacy.
Description
Keywords
computer vision, set encoding, set representation, histopathology, medical imaging, giga-pixel images, machine learning, multiple instance learning