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Exploring the utility of Bayesian Ne...

Border, Samuel Peter.

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Exploring the utility of Bayesian Networks in Histopathological Image Analysis: Beyond Classifier Networks.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Exploring the utility of Bayesian Networks in Histopathological Image Analysis: Beyond Classifier Networks./
Author:	Border, Samuel Peter.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	33 p.
Notes:	Source: Masters Abstracts International, Volume: 81-09.
Contained By:	Masters Abstracts International81-09.
Subject:	Biomedical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27736521
ISBN:	9781658421515

Exploring the utility of Bayesian Networks in Histopathological Image Analysis: Beyond Classifier Networks.
Border, Samuel Peter.

Exploring the utility of Bayesian Networks in Histopathological Image Analysis: Beyond Classifier Networks. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 33 p.

Source: Masters Abstracts International, Volume: 81-09.

Thesis (M.S.)--State University of New York at Buffalo, 2020.

This item must not be sold to any third party vendors.

Pathologists' assessment of tissue histology is primarily confined to the study select spatial features of interest for making diagnostic and/or prognostic decision. In recent years, research scientists have applied high-throughput computational methods such as neural networks (NN) capable of learning their own descriptive features to minimize prediction cost. For image related projects convolutional neural networks have proven highly effective and have surged in popularity over a variety of tasks. However, a major drawback of NN tools is the significant gap in understanding between researchers and clinicians. In a clinical environment it is important to establish the biological relevance of the feature sets that are important for informed decision making. Bayesian networks (BNs), a type of probabilistic graphical model, have not been widely adapted in computational pathology despite the wide variety of potential benefits. They allow clinicians to incorporate hand-crafted features from a wide range of modalities to assess disease progression on a continuous scale. In this thesis, we have explored the different functionalities of BNs and how they can be utilized to better understand the factors contributing to progression of Diabetic Nephropathy (DN). Our key results include DN stage classification with absolute error (AE) of 1.25 using a BN constructed from n = 1124 human DN glomeruli. This result outperforms classical machine learning methods, such as k-nearest neighbor, decision trees, and SVM. Although more modern classification methods (MLP, AdaBoost, Nearest Centroid) achieved a slightly lower AE, the benefit of BN is the ability to directly model feature relationships to impart more information on underlying disease mechanisms.

ISBN: 9781658421515Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Bayesian networks

Exploring the utility of Bayesian Networks in Histopathological Image Analysis: Beyond Classifier Networks.
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506 $a This item must not be sold to any third party vendors.
520 $a Pathologists' assessment of tissue histology is primarily confined to the study select spatial features of interest for making diagnostic and/or prognostic decision. In recent years, research scientists have applied high-throughput computational methods such as neural networks (NN) capable of learning their own descriptive features to minimize prediction cost. For image related projects convolutional neural networks have proven highly effective and have surged in popularity over a variety of tasks. However, a major drawback of NN tools is the significant gap in understanding between researchers and clinicians. In a clinical environment it is important to establish the biological relevance of the feature sets that are important for informed decision making. Bayesian networks (BNs), a type of probabilistic graphical model, have not been widely adapted in computational pathology despite the wide variety of potential benefits. They allow clinicians to incorporate hand-crafted features from a wide range of modalities to assess disease progression on a continuous scale. In this thesis, we have explored the different functionalities of BNs and how they can be utilized to better understand the factors contributing to progression of Diabetic Nephropathy (DN). Our key results include DN stage classification with absolute error (AE) of 1.25 using a BN constructed from n = 1124 human DN glomeruli. This result outperforms classical machine learning methods, such as k-nearest neighbor, decision trees, and SVM. Although more modern classification methods (MLP, AdaBoost, Nearest Centroid) achieved a slightly lower AE, the benefit of BN is the ability to directly model feature relationships to impart more information on underlying disease mechanisms.
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