THESIS
2023
1 online resource (xix, 98 pages) : color illustrations
Abstract
In recent years, there has been a growing interest in developing methods to integrate
multiple sources of information to improve decision-making in various
fields. AI techniques such as deep learning and deep reinforcement learning have
shown great potential for fusing different types of data and extracting useful features.
This thesis focuses on the use of AI techniques to fuse multiple structured
data in two different domains: medical imaging and high-frequency trading.
In the first part of the thesis, we address the challenge of extracting and segmenting
3D blood vessels from CT images. Automatic blood vessel extraction from
3D medical images is crucial for vascular disease diagnoses. Existing methods
based on convolutional neural networks (CNNs) may suffer from discontinuities
of ex...[
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In recent years, there has been a growing interest in developing methods to integrate
multiple sources of information to improve decision-making in various
fields. AI techniques such as deep learning and deep reinforcement learning have
shown great potential for fusing different types of data and extracting useful features.
This thesis focuses on the use of AI techniques to fuse multiple structured
data in two different domains: medical imaging and high-frequency trading.
In the first part of the thesis, we address the challenge of extracting and segmenting
3D blood vessels from CT images. Automatic blood vessel extraction from
3D medical images is crucial for vascular disease diagnoses. Existing methods
based on convolutional neural networks (CNNs) may suffer from discontinuities
of extracted vessels when segmenting such thin tubular structures from 3D images.
We argue that preserving the continuity of extracted vessels requires taking
into account the global geometry. However, 3D convolutions are computationally
inefficient, which prohibits the 3D CNNs from sufficiently large receptive fields
to capture the global cues in the entire image. In this part, we propose a hybrid
representation learning approach to address this challenge. The main idea
is to use CNNs to learn local appearances of vessels in image crops while using another point-cloud network to learn the global geometry of vessels in the entire
image. In inference, the proposed approach extracts local segments of vessels
using CNNs, classifies each segment based on global geometry using the point-cloud
network, and finally connects all the segments that belong to the same
vessel using the shortest-path algorithm. This combination results in an efficient,
fully-automatic, and template-free approach to centerline extraction from
3D images. We validate the proposed approach on CTA datasets and demonstrate
its superior performance compared to both traditional and CNN-based
baselines.
In the second part of the thesis, we focus on the problem of market making in
high-frequency trading. Market making is a critical function in financial markets
that involves providing liquidity by buying and selling assets. However, the
increasing complexity of financial markets and the high volume of data generated
by tick-level trading makes it challenging to develop effective market making
strategies. To address this challenge, we propose a deep reinforcement learning
approach that fuses tick-level data with periodic prediction signals to develop a
more accurate and robust market making strategy. Our results of market making
strategies based on different deep reinforcement learning algorithms under the
simulation scenarios and real data experiments in the cryptocurrency markets
show that the proposed framework outperforms existing methods in terms of
profitability and risk management.
Keywords: Medical image analysis, 3D vessel segmentation, Hybrid representations,
High-frequency trading, Market making strategy, Deep reinforcement
learning
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