81 research outputs found
๋์ข , ์ด์ข , ๊ทธ๋ฆฌ๊ณ ๋๋ฌด ํํ์ ๊ทธ๋ํ๋ฅผ ์ํ ๋น์ง๋ ํํ ํ์ต
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2022. 8. ์ต์ง์.๊ทธ๋ํ ๋ฐ์ดํฐ์ ๋ํ ๋น์ง๋ ํํ ํ์ต์ ๋ชฉ์ ์ ๊ทธ๋ํ์ ๊ตฌ์กฐ์ ๋
ธ๋์ ์์ฑ์ ์ ๋ฐ์ํ๋ ์ ์ฉํ ๋
ธ๋ ๋จ์ ํน์ ๊ทธ๋ํ ๋จ์์ ๋ฒกํฐ ํํ ํํ์ ํ์ตํ๋ ๊ฒ์ด๋ค. ์ต๊ทผ, ๊ทธ๋ํ ๋ฐ์ดํฐ์ ๋ํด ๊ฐ๋ ฅํ ํํ ํ์ต ๋ฅ๋ ฅ์ ๊ฐ์ถ ๊ทธ๋ํ ์ ๊ฒฝ๋ง์ ํ์ฉํ ๋น์ง๋ ๊ทธ๋ํ ํํ ํ์ต ๋ชจ๋ธ์ ์ค๊ณ๊ฐ ์ฃผ๋ชฉ์ ๋ฐ๊ณ ์๋ค. ๋ง์ ๋ฐฉ๋ฒ๋ค์ ํ ์ข
๋ฅ์ ์ฃ์ง์ ํ ์ข
๋ฅ์ ๋
ธ๋๊ฐ ์กด์ฌํ๋ ๋์ข
๊ทธ๋ํ์ ๋ํ ํ์ต์ ์ง์ค์ ํ๋ค. ํ์ง๋ง ์ด ์ธ์์ ์๋ง์ ์ข
๋ฅ์ ๊ด๊ณ๊ฐ ์กด์ฌํ๊ธฐ ๋๋ฌธ์, ๊ทธ๋ํ ๋ํ ๊ตฌ์กฐ์ , ์๋ฏธ๋ก ์ ์์ฑ์ ํตํด ๋ค์ํ ์ข
๋ฅ๋ก ๋ถ๋ฅํ ์ ์๋ค. ๊ทธ๋์, ๊ทธ๋ํ๋ก๋ถํฐ ์ ์ฉํ ํํ์ ํ์ตํ๊ธฐ ์ํด์๋ ๋น์ง๋ ํ์ต ํ๋ ์์ํฌ๋ ์
๋ ฅ ๊ทธ๋ํ์ ํน์ง์ ์ ๋๋ก ๊ณ ๋ คํด์ผ๋ง ํ๋ค. ๋ณธ ํ์๋
ผ๋ฌธ์์ ์ฐ๋ฆฌ๋ ๋๋ฆฌ ์ ํ ์ ์๋ ์ธ๊ฐ์ง ๊ทธ๋ํ ๊ตฌ์กฐ์ธ ๋์ข
๊ทธ๋ํ, ํธ๋ฆฌ ํํ์ ๊ทธ๋ํ, ๊ทธ๋ฆฌ๊ณ ์ด์ข
๊ทธ๋ํ์ ๋ํ ๊ทธ๋ํ ์ ๊ฒฝ๋ง์ ํ์ฉํ๋ ๋น์ง๋ ํ์ต ๋ชจ๋ธ๋ค์ ์ ์ํ๋ค.
์ฒ์์ผ๋ก, ์ฐ๋ฆฌ๋ ๋์ข
๊ทธ๋ํ์ ๋
ธ๋์ ๋ํ์ฌ ์ ์ฐจ์ ํํ์ ํ์ตํ๋ ๊ทธ๋ํ ์ปจ๋ณผ๋ฃจ์
์คํ ์ธ์ฝ๋ ๋ชจ๋ธ์ ์ ์ํ๋ค. ๊ธฐ์กด์ ๊ทธ๋ํ ์คํ ์ธ์ฝ๋๋ ๊ตฌ์กฐ์ ์ ์ฒด๊ฐ ํ์ต์ด ๋ถ๊ฐ๋ฅํด์ ์ ํ์ ์ธ ํํ ํ์ต ๋ฅ๋ ฅ์ ๊ฐ์ง ์ ์๋ ๋ฐ๋ฉด์, ์ ์ํ๋ ์คํ ์ธ์ฝ๋๋ ๋
ธ๋์ ํผ์ณ๋ฅผ ๋ณต์ํ๋ฉฐ,๊ตฌ์กฐ์ ์ ์ฒด๊ฐ ํ์ต์ด ๊ฐ๋ฅํ๋ค. ๋
ธ๋์ ํผ์ณ๋ฅผ ๋ณต์ํ๊ธฐ ์ํด์, ์ฐ๋ฆฌ๋ ์ธ์ฝ๋ ๋ถ๋ถ์ ์ญํ ์ด ์ด์ํ ๋
ธ๋๋ผ๋ฆฌ ์ ์ฌํ ํํ์ ๊ฐ์ง๊ฒ ํ๋ ๋ผํ๋ผ์์ ์ค๋ฌด๋ฉ์ด๋ผ๋ ๊ฒ์ ์ฃผ๋ชฉํ์ฌ ๋์ฝ๋ ๋ถ๋ถ์์๋ ์ด์ ๋
ธ๋์ ํํ๊ณผ ๋ฉ์ด์ง๊ฒ ํ๋ ๋ผํ๋ผ์์ ์คํ๋์ ํ๋๋ก ์ค๊ณํ์๋ค. ๋ํ ๋ผํ๋ผ์์ ์คํ๋์ ๊ทธ๋๋ก ์ ์ฉํ๋ฉด ๋ถ์์ ์ฑ์ ์ ๋ฐํ ์ ์๊ธฐ ๋๋ฌธ์, ์ฃ์ง์ ๊ฐ์ค์น ๊ฐ์ ์์ ๊ฐ์ ์ค ์ ์๋ ๋ถํธํ ๊ทธ๋ํ๋ฅผ ํ์ฉํ์ฌ ์์ ์ ์ธ ๋ผํ๋ผ์์ ์คํ๋์ ํํ๋ฅผ ์ ์ํ์๋ค. ๋์ข
๊ทธ๋ํ์ ๋ํ ๋
ธ๋ ํด๋ฌ์คํฐ๋ง๊ณผ ๋งํฌ ์์ธก ์คํ์ ํตํ์ฌ ์ ์ํ๋ ๋ฐฉ๋ฒ์ด ์์ ์ ์ผ๋ก ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์์ ํ์ธํ์๋ค.
๋์งธ๋ก, ์ฐ๋ฆฌ๋ ํธ๋ฆฌ์ ํํ๋ฅผ ๊ฐ์ง๋ ๊ณ์ธต์ ์ธ ๊ด๊ณ๋ฅผ ๊ฐ์ง๊ณ ์๋ ๊ทธ๋ํ์ ๋
ธ๋ ํํ์ ์ ํํ๊ฒ ํ์ตํ๊ธฐ ์ํ์ฌ ์๊ณก์ ๊ณต๊ฐ์์ ๋์ํ๋ ์คํ ์ธ์ฝ๋ ๋ชจ๋ธ์ ์ ์ํ๋ค. ์ ํด๋ฆฌ๋์ธ ๊ณต๊ฐ์ ํธ๋ฆฌ๋ฅผ ์ฌ์ํ๊ธฐ์ ๋ถ์ ์ ํ๋ค๋ ์ต๊ทผ์ ๋ถ์์ ํตํ์ฌ, ์๊ณก์ ๊ณต๊ฐ์์ ๊ทธ๋ํ ์ ๊ฒฝ๋ง์ ๋ ์ด์ด๋ฅผ ํ์ฉํ์ฌ ๋
ธ๋์ ์ ์ฐจ์ ํํ์ ํ์ตํ๊ฒ ๋๋ค. ์ด ๋, ๊ทธ๋ํ ์ ๊ฒฝ๋ง์ด ์๊ณก์ ๊ธฐํํ์์ ๊ณ์ธต ์ ๋ณด๋ฅผ ๋ด๊ณ ์๋ ๊ฑฐ๋ฆฌ์ ๊ฐ์ ํ์ฉํ์ฌ ๋
ธ๋์ ์ด์์ฌ์ด์ ์ค์๋๋ฅผ ํ์ฉํ๋๋ก ์ค๊ณํ์๋ค. ์ฐ๋ฆฌ๋ ๋
ผ๋ฌธ ์ธ์ฉ ๊ด๊ณ ๋คํธ์ํฌ, ๊ณํต๋, ์ด๋ฏธ์ง ์ฌ์ด์ ๋คํธ์ํฌ๋ฑ์ ๋ํด ์ ์ํ ๋ชจ๋ธ์ ์ ์ฉํ์ฌ ๋
ธ๋ ํด๋ฌ์คํฐ๋ง๊ณผ ๋งํฌ ์์ธก ์คํ์ ํ์์ผ๋ฉฐ, ํธ๋ฆฌ์ ํํ๋ฅผ ๊ฐ์ง๋ ๊ทธ๋ํ์ ๋ํด์ ์ ์ํ ๋ชจ๋ธ์ด ์ ํด๋ฆฌ๋์ธ ๊ณต๊ฐ์์ ์ํํ๋ ๋ชจ๋ธ์ ๋นํด ํฅ์๋ ์ฑ๋ฅ์ ๋ณด์๋ค๋ ๊ฒ์ ํ์ธํ์๋ค.
๋ง์ง๋ง์ผ๋ก, ์ฐ๋ฆฌ๋ ์ฌ๋ฌ ์ข
๋ฅ์ ๋
ธ๋์ ์ฃ์ง๋ฅผ ๊ฐ์ง๋ ์ด์ข
๊ทธ๋ํ์ ๋ํ ๋์กฐ ํ์ต ๋ชจ๋ธ์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๋ ๊ธฐ์กด์ ๋ฐฉ๋ฒ๋ค์ด ํ์ตํ๊ธฐ ์ด์ ์ ์ถฉ๋ถํ ๋๋ฉ์ธ ์ง์์ ์ฌ์ฉํ์ฌ ์ค๊ณํ ๋ฉํํจ์ค๋ ๋ฉํ๊ทธ๋ํ์ ์์กดํ๋ค๋ ๋จ์ ๊ณผ ๋ง์ ์ด์ข
๊ทธ๋ํ์ ์ฃ์ง๊ฐ ๋ค๋ฅธ ๋
ธ๋ ์ข
๋ฅ์ฌ์ด์ ๊ด๊ณ์ ์ง์คํ๊ณ ์๋ค๋ ์ ์ ์ฃผ๋ชฉํ์๋ค. ์ด๋ฅผ ํตํด ์ฐ๋ฆฌ๋ ์ฌ์ ๊ณผ์ ์ด ํ์์์ผ๋ฉฐ ๋ค๋ฅธ ์ข
๋ฅ ์ฌ์ด์ ๊ด๊ณ์ ๋ํ์ฌ ๊ฐ์ ์ข
๋ฅ ์ฌ์ด์ ๊ด๊ณ๋ ๋์์ ํจ์จ์ ์ผ๋ก ํ์ตํ๊ฒ ํ๋ ๋ฉํ๋
ธ๋๋ผ๋ ๊ฐ๋
์ ์ ์ํ์๋ค. ๋ํ ๋ฉํ๋
ธ๋๋ฅผ ๊ธฐ๋ฐ์ผ๋กํ๋ ๊ทธ๋ํ ์ ๊ฒฝ๋ง๊ณผ ๋์กฐ ํ์ต ๋ชจ๋ธ์ ์ ์ํ์๋ค. ์ฐ๋ฆฌ๋ ์ ์ํ ๋ชจ๋ธ์ ๋ฉํํจ์ค๋ฅผ ์ฌ์ฉํ๋ ์ด์ข
๊ทธ๋ํ ํ์ต ๋ชจ๋ธ๊ณผ ๋
ธ๋ ํด๋ฌ์คํฐ๋ง ๋ฑ์ ์คํ ์ฑ๋ฅ์ผ๋ก ๋น๊ตํด๋ณด์์ ๋, ๋น๋ฑํ๊ฑฐ๋ ๋์ ์ฑ๋ฅ์ ๋ณด์์์ ํ์ธํ์๋ค.The goal of unsupervised graph representation learning is extracting useful node-wise or graph-wise vector representation that is aware of the intrinsic structures of the graph and its attributes. These days, designing methodology of unsupervised graph representation learning based on graph neural networks has growing attention due to their powerful representation ability. Many methods are focused on a homogeneous graph that is a network with a single type of node and a single type of edge. However, as many types of relationships exist in this world, graphs can also be classified into various types by structural and semantic properties. For this reason, to learn useful representations from graphs, the unsupervised learning framework must consider the characteristics of the input graph. In this dissertation, we focus on designing unsupervised learning models using graph neural networks for three graph structures that are widely available: homogeneous graphs, tree-like graphs, and heterogeneous graphs.
First, we propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a homogeneous graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. The experimental results of clustering, link prediction and visualization tasks on homogeneous graphs strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms.
Second, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing autoencoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations of tree-like graphs.
Third, we propose the novel concept of metanode for message passing to learn both heterogeneous and homogeneous relationships between any two nodes without meta-paths and meta-graphs. Unlike conventional methods, metanodes do not require a predetermined step to manipulate the given relations between different types to enrich relational information. Going one step further, we propose a metanode-based message passing layer and a contrastive learning model using the proposed layer. In our experiments, we show the competitive performance of the proposed metanode-based message passing method on node clustering and node classification tasks, when compared to state-of-the-art methods for message passing networks for heterogeneous graphs.1 Introduction 1
2 Representation Learning on Graph-Structured Data 4
2.1 Basic Introduction 4
2.1.1 Notations 5
2.2 Traditional Approaches 5
2.2.1 Graph Statistic 5
2.2.2 Neighborhood Overlap 7
2.2.3 Graph Kernel 9
2.2.4 Spectral Approaches 10
2.3 Node Embeddings I: Factorization and Random Walks 15
2.3.1 Factorization-based Methods 15
2.3.2 Random Walk-based Methods 16
2.4 Node Embeddings II: Graph Neural Networks 17
2.4.1 Overview of Framework 17
2.4.2 Representative Models 18
2.5 Learning in Unsupervised Environments 21
2.5.1 Predictive Coding 21
2.5.2 Contrastive Coding 22
2.6 Applications 24
2.6.1 Classifications 24
2.6.2 Link Prediction 26
3 Autoencoder Architecture for Homogeneous Graphs 27
3.1 Overview 27
3.2 Preliminaries 30
3.2.1 Spectral Convolution on Graphs 30
3.2.2 Laplacian Smoothing 32
3.3 Methodology 33
3.3.1 Laplacian Sharpening 33
3.3.2 Numerically Stable Laplacian Sharpening 34
3.3.3 Subspace Clustering Cost for Image Clustering 37
3.3.4 Training 39
3.4 Experiments 40
3.4.1 Datasets 40
3.4.2 Experimental Settings 42
3.4.3 Comparing Methods 42
3.4.4 Node Clustering 43
3.4.5 Image Clustering 45
3.4.6 Ablation Studies 46
3.4.7 Link Prediction 47
3.4.8 Visualization 47
3.5 Summary 49
4 Autoencoder Architecture for Tree-like Graphs 50
4.1 Overview 50
4.2 Preliminaries 52
4.2.1 Hyperbolic Embeddings 52
4.2.2 Hyperbolic Geometry 53
4.3 Methodology 55
4.3.1 Geometry-Aware Message Passing 56
4.3.2 Nonlinear Activation 57
4.3.3 Loss Function 58
4.4 Experiments 58
4.4.1 Datasets 59
4.4.2 Compared Methods 61
4.4.3 Experimental Details 62
4.4.4 Node Clustering and Link Prediction 64
4.4.5 Image Clustering 66
4.4.6 Structure-Aware Unsupervised Embeddings 68
4.4.7 Hyperbolic Distance to Filter Training Samples 71
4.4.8 Ablation Studies 74
4.5 Further Discussions 75
4.5.1 Connection to Contrastive Learning 75
4.5.2 Failure Cases of Hyperbolic Embedding Spaces 75
4.6 Summary 77
5 Contrastive Learning for Heterogeneous Graphs 78
5.1 Overview 78
5.2 Preliminaries 82
5.2.1 Meta-path 82
5.2.2 Representation Learning on Heterogeneous Graphs 82
5.2.3 Contrastive methods for Heterogeneous Graphs 83
5.3 Methodology 84
5.3.1 Definitions 84
5.3.2 Metanode-based Message Passing Layer 86
5.3.3 Contrastive Learning Framework 88
5.4 Experiments 89
5.4.1 Experimental Details 90
5.4.2 Node Classification 94
5.4.3 Node Clustering 96
5.4.4 Visualization 96
5.4.5 Effectiveness of Metanodes 97
5.5 Summary 99
6 Conclusions 101๋ฐ
Field theoretic formulation and empirical tracking of spatial processes
Spatial processes are attacked on two fronts. On the one hand, tools from theoretical and
statistical physics can be used to understand behaviour in complex, spatially-extended
multi-body systems. On the other hand, computer vision and statistical analysis can be
used to study 4D microscopy data to observe and understand real spatial processes in
vivo.
On the rst of these fronts, analytical models are developed for abstract processes, which
can be simulated on graphs and lattices before considering real-world applications in elds
such as biology, epidemiology or ecology. In the eld theoretic formulation of spatial processes,
techniques originating in quantum eld theory such as canonical quantisation and
the renormalization group are applied to reaction-di usion processes by analogy. These
techniques are combined in the study of critical phenomena or critical dynamics. At this
level, one is often interested in the scaling behaviour; how the correlation functions scale
for di erent dimensions in geometric space. This can lead to a better understanding of how
macroscopic patterns relate to microscopic interactions. In this vein, the trace of a branching
random walk on various graphs is studied. In the thesis, a distinctly abstract approach
is emphasised in order to support an algorithmic approach to parts of the formalism.
A model of self-organised criticality, the Abelian sandpile model, is also considered. By
exploiting a bijection between recurrent con gurations and spanning trees, an e cient
Monte Carlo algorithm is developed to simulate sandpile processes on large lattices.
On the second front, two case studies are considered; migratory patterns of leukaemia cells
and mitotic events in Arabidopsis roots. In the rst case, tools from statistical physics
are used to study the spatial dynamics of di erent leukaemia cell lineages before and after
a treatment. One key result is that we can discriminate between migratory patterns in
response to treatment, classifying cell motility in terms of sup/super/di usive regimes.
For the second case study, a novel algorithm is developed to processes a 4D light-sheet
microscopy dataset. The combination of transient uorescent markers and a poorly localised
specimen in the eld of view leads to a challenging tracking problem. A fuzzy
registration-tracking algorithm is developed to track mitotic events so as to understand
their spatiotemporal dynamics under normal conditions and after tissue damage.Open Acces
2D growth processes: SLE and Loewner chains
This review provides an introduction to two dimensional growth processes.
Although it covers a variety processes such as diffusion limited aggregation,
it is mostly devoted to a detailed presentation of stochastic Schramm-Loewner
evolutions (SLE) which are Markov processes describing interfaces in 2D
critical systems. It starts with an informal discussion, using numerical
simulations, of various examples of 2D growth processes and their connections
with statistical mechanics. SLE is then introduced and Schramm's argument
mapping conformally invariant interfaces to SLE is explained. A substantial
part of the review is devoted to reveal the deep connections between
statistical mechanics and processes, and more specifically to the present
context, between 2D critical systems and SLE. Some of the SLE remarkable
properties are explained, as well as the tools for computing with SLE. This
review has been written with the aim of filling the gap between the
mathematical and the physical literatures on the subject.Comment: A review on Stochastic Loewner evolutions for Physics Reports, 172
pages, low quality figures, better quality figures upon request to the
authors, comments welcom
Computational imaging and automated identification for aqueous environments
Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods.
Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection.
Methods for extracting images of sh from videos of longline operations are demonstrated.
A prototype digital holographic imaging device is designed and tested for quantitative
in situ microscale imaging. Theory to support the device is developed, including particle
noise and the effects of motion. A Wigner-domain model provides optimal settings and
optical limits for spherical and planar holographic references.
Algorithms to extract the information from real-world digital holograms are created.
Focus metrics are discussed, including a novel focus detector using local Zernike moments.
Two methods for estimating lateral positions of objects in holograms without reconstruction
are presented by extending a summation kernel to spherical references and using a local
frequency signature from a Riesz transform. A new metric for quickly estimating object
depths without reconstruction is proposed and tested. An example application, quantifying
oil droplet size distributions in an underwater plume, demonstrates the efficacy of the
prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008
Ultrasound imaging system combined with multi-modality image analysis algorithms to monitor changes in anatomical structures
This dissertation concerns the development and validation of an ultrasound imaging system and novel image analysis algorithms applicable to multiple imaging modalities. The ultrasound imaging system will include a framework for 3D volume reconstruction of freehand ultrasound: a mechanism to register the 3D volumes across time and subjects, as well as with other imaging modalities, and a playback mechanism to view image slices concurrently from different acquisitions that correspond to the same anatomical region. The novel image analysis algorithms include a noise reduction method that clusters pixels into homogenous patches using a directed graph of edges between neighboring pixels, a segmentation method that creates a hierarchical graph structure using statistical analysis and a voting system to determine the similarity between homogeneous patches given their neighborhood, and finally, a hybrid atlas-based registration method that makes use of intensity corrections induced at anatomical landmarks to regulate deformable registration. The combination of the ultrasound imaging system and the image analysis algorithms will provide the ability to monitor nerve regeneration in patients undergoing regenerative, repair or transplant strategies in a sequential, non-invasive manner, including visualization of registered real-time and pre-acquired data, thus enabling preventive and therapeutic strategies for nerve regeneration in Composite Tissue Allotransplantation (CTA). The registration algorithm is also applied to MR images of the brain to obtain reliable and efficient segmentation of the hippocampus, which is a prominent structure in the study of diseases of the elderly such as vascular dementia, Alzheimerโs, and late life depression. Experimental results on 2D and 3D images, including simulated and real images, with illustrations visualizing the intermediate outcomes and the final results are presented.
Risk prediction analysis for post-surgical complications in cardiothoracic surgery
Cardiothoracic surgery patients have the risk of developing surgical site infections
(SSIs), which causes hospital readmissions, increases healthcare costs and may lead to
mortality. The first 30 days after hospital discharge are crucial for preventing these
kind of infections. As an alternative to a hospital-based diagnosis, an automatic digital
monitoring system can help with the early detection of SSIs by analyzing daily images
of patientโs wounds. However, analyzing a wound automatically is one of the biggest
challenges in medical image analysis.
The proposed system is integrated into a research project called CardioFollowAI,
which developed a digital telemonitoring service to follow-up the recovery of cardiothoracic
surgery patients. This present work aims to tackle the problem of SSIs by predicting
the existence of worrying alterations in wound images taken by patients, with the help of
machine learning and deep learning algorithms. The developed system is divided into a
segmentation model which detects the wound region area and categorizes the wound type,
and a classification model which predicts the occurrence of alterations in the wounds.
The dataset consists of 1337 images with chest wounds (WC), drainage wounds (WD)
and leg wounds (WL) from 34 cardiothoracic surgery patients. For segmenting the images,
an architecture with a Mobilenet encoder and an Unet decoder was used to obtain
the regions of interest (ROI) and attribute the wound class. The following model was
divided into three sub-classifiers for each wound type, in order to improve the modelโs
performance. Color and textural features were extracted from the woundโs ROIs to feed
one of the three machine learning classifiers (random Forest, support vector machine and
K-nearest neighbors), that predict the final output.
The segmentation model achieved a final mean IoU of 89.9%, a dice coefficient of
94.6% and a mean average precision of 90.1%, showing good results. As for the algorithms
that performed classification, the WL classifier exhibited the best results with a
87.6% recall and 52.6% precision, while WC classifier achieved a 71.4% recall and 36.0%
precision. The WD had the worst performance with a 68.4% recall and 33.2% precision.
The obtained results demonstrate the feasibility of this solution, which can be a start for
preventing SSIs through image analysis with artificial intelligence.Os pacientes submetidos a uma cirurgia cardiotorรกcica tem o risco de desenvolver
infeรงรตes no local da ferida cirรบrgica, o que pode consequentemente levar a readmissรตes
hospitalares, ao aumento dos custos na saรบde e ร mortalidade. Os primeiros 30 dias
apรณs a alta hospitalar sรฃo cruciais na prevenรงรฃo destas infecรงรตes. Assim, como alternativa
ao diagnรณstico no hospital, a utilizaรงรฃo diรกria de um sistema digital e automรกtico de
monotorizaรงรฃo em imagens de feridas cirรบrgicas pode ajudar na precoce deteรงรฃo destas
infeรงรตes. No entanto, a anรกlise automรกtica de feridas รฉ um dos grandes desafios em anรกlise
de imagens mรฉdicas.
O sistema proposto integra um projeto de investigaรงรฃo designado CardioFollow.AI,
que desenvolveu um serviรงo digital de telemonitorizaรงรฃo para realizar o follow-up da recuperaรงรฃo
dos pacientes de cirurgia cardiotorรกcica. Neste trabalho, o problema da infeรงรฃo
de feridas cirรบrgicas รฉ abordado, atravรฉs da deteรงรฃo de alteraรงรตes preocupantes na ferida
com ajuda de algoritmos de aprendizagem automรกtica. O sistema desenvolvido divide-se
num modelo de segmentaรงรฃo, que deteta a regiรฃo da ferida e a categoriza consoante o seu
tipo, e num modelo de classificaรงรฃo que prevรช a existรชncia de alteraรงรตes na ferida.
O conjunto de dados consistiu em 1337 imagens de feridas do peito (WC), feridas
dos tubos de drenagem (WD) e feridas da perna (WL), provenientes de 34 pacientes de
cirurgia cardiotorรกcica. A segmentaรงรฃo de imagem foi realizada atravรฉs da combinaรงรฃo
de Mobilenet como codificador e Unet como decodificador, de forma a obter-se as regiรตes
de interesse e atribuir a classe da ferida. O modelo seguinte foi dividido em trรชs subclassificadores
para cada tipo de ferida, de forma a melhorar a performance do modelo.
Caraterรญsticas de cor e textura foram extraรญdas da regiรฃo da ferida para serem introduzidas
num dos modelos de aprendizagem automรกtica de forma a prever a classificaรงรฃo final
(Random Forest, Support Vector Machine and K-Nearest Neighbors).
O modelo de segmentaรงรฃo demonstrou bons resultados ao obter um IoU mรฉdio final
de 89.9%, um dice de 94.6% e uma mรฉdia de precisรฃo de 90.1%. Relativamente aos algoritmos
que realizaram a classificaรงรฃo, o classificador WL exibiu os melhores resultados
com 87.6% de recall e 62.6% de precisรฃo, enquanto o classificador das WC conseguiu um recall de 71.4% e 36.0% de precisรฃo. Por fim, o classificador das WD teve a pior performance
com um recall de 68.4% e 33.2% de precisรฃo. Os resultados obtidos demonstram
a viabilidade desta soluรงรฃo, que constitui o inรญcio da prevenรงรฃo de infeรงรตes em feridas
cirรบrgica a partir da anรกlise de imagem, com recurso a inteligรชncia artificial
3D shape instantiation for intra-operative navigation from a single 2D projection
Unlike traditional open surgery where surgeons can see the operation area clearly, in robot-assisted Minimally Invasive Surgery (MIS), a surgeonโs view of the region of interest is usually limited. Currently, 2D images from fluoroscopy, Magnetic Resonance Imaging (MRI), endoscopy or ultrasound are used for intra-operative guidance as real-time 3D volumetric acquisition is not always possible due to the acquisition speed or exposure constraints. 3D reconstruction, however, is key to navigation in complex in vivo geometries and can help resolve this issue. Novel 3D shape instantiation schemes are developed in this thesis, which can reconstruct the high-resolution 3D shape of a target from limited 2D views, especially a single 2D projection or slice. To achieve a complete and automatic 3D shape instantiation pipeline, segmentation schemes based on deep learning are also investigated. These include normalization schemes for training U-Nets and network architecture design of Atrous Convolutional Neural Networks (ACNNs).
For U-Net normalization, four popular normalization methods are reviewed, then Instance-Layer Normalization (ILN) is proposed. It uses a sigmoid function to linearly weight the feature map after instance normalization and layer normalization, and cascades group normalization after the weighted feature map. Detailed validation results potentially demonstrate the practical advantages of the proposed ILN for effective and robust segmentation of different anatomies.
For network architecture design in training Deep Convolutional Neural Networks (DCNNs), the newly proposed ACNN is compared to traditional U-Net where max-pooling and deconvolutional layers are essential. Only convolutional layers are used in the proposed ACNN with different atrous rates and it has been shown that the method is able to provide a fully-covered receptive field with a minimum number of atrous convolutional layers. ACNN enhances the robustness and generalizability of the analysis scheme by cascading multiple atrous blocks. Validation results have shown the proposed method achieves comparable results to the U-Net in terms of medical image segmentation, whilst reducing the trainable parameters, thus improving the convergence and real-time instantiation speed.
For 3D shape instantiation of soft and deforming organs during MIS, Sparse Principle Component Analysis (SPCA) has been used to analyse a 3D Statistical Shape Model (SSM) and to determine the most informative scan plane. Synchronized 2D images are then scanned at the most informative scan plane and are expressed in a 2D SSM. Kernel Partial Least Square Regression (KPLSR) has been applied to learn the relationship between the 2D and 3D SSM. It has been shown that the KPLSR-learned model developed in this thesis is able to predict the intra-operative 3D target shape from a single 2D projection or slice, thus permitting real-time 3D navigation. Validation results have shown the intrinsic accuracy achieved and the potential clinical value of the technique.
The proposed 3D shape instantiation scheme is further applied to intra-operative stent graft deployment for the robot-assisted treatment of aortic aneurysms. Mathematical modelling is first used to simulate the stent graft characteristics. This is then followed by the Robust Perspective-n-Point (RPnP) method to instantiate the 3D pose of fiducial markers of the graft. Here, Equally-weighted Focal U-Net is proposed with a cross-entropy and an additional focal loss function. Detailed validation has been performed on patient-specific stent grafts with an accuracy between 1-3mm. Finally, the relative merits and potential pitfalls of all the methods developed in this thesis are discussed, followed by potential future research directions and additional challenges that need to be tackled.Open Acces
Computational imaging and automated identification for aqueous environments
Thesis (Ph. D.)--Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and the Woods Hole Oceanographic Institution), 2011."June 2011." Cataloged from PDF version of thesis.Includes bibliographical references (p. 253-293).Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classification with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of long-line operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.by Nicholas C. Loomis.Ph.D
FAST LEARNING ON GRAPHS
We carry out a systematic study of classification problems on networked data,
presenting novel techniques with good performance both in theory and in
practice.
We assess the power of node classification based on class-linkage information
only. In particular, we propose four new algorithms that exploit the
homiphilic bias (linked entities tend to belong to the same class) in different
ways.
The set of the algorithms we present covers diverse practical needs: some
of them operate in an active transductive setting and others in an on-line
transductive setting. A third group works within an explorative protocol,
in which the vertices of an unknown graph are progressively revealed to the
learner in an on-line fashion.
Within the mistake bound learning model, for each of our algorithms
we provide a rigorous theoretical analysis, together with an interpretation
of the obtained performance bounds. We also design adversarial strategies
achieving matching lower bounds. In particular, we prove optimality for all
input graphs and for all fixed regularity values of suitable labeling complexity
measures. We also analyze the computational requirements of our methods,
showing that our algorithms can to handle very large data sets.
In the case of the on-line protocol, for which we exhibit an optimal algorithm
with constant amortized time per prediction, we validate our theoretical
results carrying out experiments on real-world datasets
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