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Deep Learning for Digitized Histology Image Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Digitized Histology Image Analysis./
Author:	Sornapudi, Sudhir.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	145 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
Contained By:	Dissertations Abstracts International83-11B.
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28023715
ISBN:	9798426819580

Deep Learning for Digitized Histology Image Analysis.
Sornapudi, Sudhir.

Deep Learning for Digitized Histology Image Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 145 p.

Source: Dissertations Abstracts International, Volume: 83-11, Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2020.

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

Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In this research, different facets of an automated digitized histology epithelium assessment pipeline have been explored to mimic the pathologist diagnostic approach. The entire pipeline from slide to epithelium CIN grade has been designed and developed using deep learning models and imaging techniques to analyze the whole slide image (WSI). The process is as follows: 1) identification of epithelium by filtering the regions extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based nuclei detection by superpixels was performed as an extension of our research. Results from this research indicate an improved performance of CIN assessment over state-of-the-art methods for nuclei segmentation, epithelium segmentation, and CIN classification, as well as the development of a prototype WSI-level tool.

ISBN: 9798426819580Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Cervical cancer

Deep Learning for Digitized Histology Image Analysis.
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245 1 0 $a Deep Learning for Digitized Histology Image Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 145 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-11, Section: B.
500 $a Advisor: Stanley, R. Joe.
502 $a Thesis (Ph.D.)--Missouri University of Science and Technology, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Cervical cancer is the fourth most frequent cancer that affects women worldwide. Assessment of cervical intraepithelial neoplasia (CIN) through histopathology remains as the standard for absolute determination of cancer. The examination of tissue samples under a microscope requires considerable time and effort from expert pathologists. There is a need to design an automated tool to assist pathologists for digitized histology slide analysis. Pre-cervical cancer is generally determined by examining the CIN which is the growth of atypical cells from the basement membrane (bottom) to the top of the epithelium. It has four grades, including: Normal, CIN1, CIN2, and CIN3. In this research, different facets of an automated digitized histology epithelium assessment pipeline have been explored to mimic the pathologist diagnostic approach. The entire pipeline from slide to epithelium CIN grade has been designed and developed using deep learning models and imaging techniques to analyze the whole slide image (WSI). The process is as follows: 1) identification of epithelium by filtering the regions extracted from a low-resolution image with a binary classifier network; 2) epithelium segmentation; 3) deep regression for pixel-wise segmentation of epithelium by patch-based image analysis; 4) attention-based CIN classification with localized sequential feature modeling. Deep learning-based nuclei detection by superpixels was performed as an extension of our research. Results from this research indicate an improved performance of CIN assessment over state-of-the-art methods for nuclei segmentation, epithelium segmentation, and CIN classification, as well as the development of a prototype WSI-level tool.
590 $a School code: 0587.
650 4 $a Computer engineering. $3 621879
650 4 $a Artificial intelligence. $3 516317
650 4 $a Medical imaging. $3 3172799
650 4 $a Oncology. $3 751006
653 $a Cervical cancer
653 $a Convolutional neural networks
653 $a Deep learning
653 $a Digital pathology
653 $a Histopathology
653 $a Whole slide image
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690 $a 0800
690 $a 0574
690 $a 0992
710 2 $a Missouri University of Science and Technology. $b Computer Engineering. $3 3182167
773 0 $t Dissertations Abstracts International $g 83-11B.
790 $a 0587
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28023715