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Deep Learning for Localizing and Segmenting Anatomies in Medical Imaging.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Learning for Localizing and Segmenting Anatomies in Medical Imaging./
Author:	Ma, Tianyu.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	125 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
Contained By:	Dissertations Abstracts International83-12B.
Subject:	Computer engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29163650
ISBN:	9798819367612

Deep Learning for Localizing and Segmenting Anatomies in Medical Imaging.
Ma, Tianyu.

Deep Learning for Localizing and Segmenting Anatomies in Medical Imaging. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 125 p.

Source: Dissertations Abstracts International, Volume: 83-12, Section: B.

Thesis (Ph.D.)--Cornell University, 2022.

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

Machine learning and deep learning have recently witnessed great successes in various fields such as computer vision and natural language processing. In many image analysis applications, deep learning and neural networks have achieved state-of-the-art results, thanks to hardware advancement and improved data quality and accessibility over the past decades. Deep learning based methods such as convolutional neural networks (CNNs) and Transformer have shown better than human performance in some visual recognition tasks including medical imaging analysis. Among all the applications that machine learning algorithms show great potential in, localizing and segmenting anatomies in medical images is one of the most important, and usually the first step before any subsequent tasks such as computer-aided diagnosis. Although there are many successful deep learning methods for keypoint detection and image segmentation, a lot of them focus on natural images, which is very different from medical imaging such as CT and MRI. Indeed, there are still many challenges we face today that hamper the adoption of deep learning in the hospital and other clinical settings. In this thesis, we talk about some of those limitations deep learning has in the field of medical image analysis, and our algorithmic innovations and solutions to these challenges.

ISBN: 9798819367612Subjects--Topical Terms:

621879
Computer engineering.
Subjects--Index Terms:

Computer vision

Deep Learning for Localizing and Segmenting Anatomies in Medical Imaging.
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300 $a 125 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-12, Section: B.
500 $a Advisor: Sabuncu, Mert.
502 $a Thesis (Ph.D.)--Cornell University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a Machine learning and deep learning have recently witnessed great successes in various fields such as computer vision and natural language processing. In many image analysis applications, deep learning and neural networks have achieved state-of-the-art results, thanks to hardware advancement and improved data quality and accessibility over the past decades. Deep learning based methods such as convolutional neural networks (CNNs) and Transformer have shown better than human performance in some visual recognition tasks including medical imaging analysis. Among all the applications that machine learning algorithms show great potential in, localizing and segmenting anatomies in medical images is one of the most important, and usually the first step before any subsequent tasks such as computer-aided diagnosis. Although there are many successful deep learning methods for keypoint detection and image segmentation, a lot of them focus on natural images, which is very different from medical imaging such as CT and MRI. Indeed, there are still many challenges we face today that hamper the adoption of deep learning in the hospital and other clinical settings. In this thesis, we talk about some of those limitations deep learning has in the field of medical image analysis, and our algorithmic innovations and solutions to these challenges.
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