國立高雄大學圖資館 |

Language: English

Back

Deep Structured Learning in Medical ...

Kong, Bin .

Deep Structured Learning in Medical Image Analysis.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Deep Structured Learning in Medical Image Analysis.
Author:	Kong, Bin .
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2020
Description:	106 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
Notes:	Advisor: Zhang, Shaoting.
Contained By:	Dissertations Abstracts International81-10B.
Subject:	Medical imaging.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27666589
ISBN:	9781658415903

Deep Structured Learning in Medical Image Analysis.
Kong, Bin .

Deep Structured Learning in Medical Image Analysis. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 106 p.

Source: Dissertations Abstracts International, Volume: 81-10, Section: B.

Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2020.

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

Deep learning-based techniques have been widely employed in solving various medical image analytical problems. Currently, most of these methods directly employ deep architectures from natural image scenarios without considering the specific structures in the input/output variables, resulting in a suboptimal solution. In this dissertation, we systematically discuss deep structured learning in medical image analysis. Particularly, this dissertation is organized by answering the following questions: 1) how to model complex dependencies among the input/output variables with deep neural networks, 2) how to enforce prior structural knowledge in deep structured learning, and 3) how to model certain special structures in medical image analytical problems. More specifically, we first introduce a formal formulation of structured learning in medical imaging and present a general deep structured learning framework to address this problem. Second, we enforce the prior structural knowledge in the loss function to further improve the analytical performance. Third, as an example of special structures in medical imaging, we introduce how to model the tree structures in coronary arteries with tree-structured convolutional long short-term memory. Finally, we further introduce a special structured learning problem in medical imaging which involves sequential decision making. Accordingly, a deep reinforcement learning-based solution is proposed. To put our discussion in the context of medical imaging, we evaluated our approaches on several medical image analytical tasks, i.e., cardiac recognizing from MRI sequences, metastasis detection in whole-slide images (WSIs), coronary artery segmentation from 3D computed tomography angiography (CTA) volumes, and axon tracing. The superior performance demonstrates the effectiveness of the proposed methods.

ISBN: 9781658415903Subjects--Topical Terms:

766168
Medical imaging.
Subjects--Index Terms:

Medical image analysis

Deep Structured Learning in Medical Image Analysis.
LDR:02906nmm a2200325 4500 001 594525
005 20210521101646.5
008 210917s2020 ||||||||||||||||| ||eng d
020 $a 9781658415903
035 $a (MiAaPQ)AAI27666589
035 $a AAI27666589
040 $a MiAaPQ $c MiAaPQ
100 1 $a Kong, Bin . $3 886514
245 1 0 $a Deep Structured Learning in Medical Image Analysis.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 106 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-10, Section: B.
500 $a Advisor: Zhang, Shaoting.
502 $a Thesis (Ph.D.)--The University of North Carolina at Charlotte, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Deep learning-based techniques have been widely employed in solving various medical image analytical problems. Currently, most of these methods directly employ deep architectures from natural image scenarios without considering the specific structures in the input/output variables, resulting in a suboptimal solution. In this dissertation, we systematically discuss deep structured learning in medical image analysis. Particularly, this dissertation is organized by answering the following questions: 1) how to model complex dependencies among the input/output variables with deep neural networks, 2) how to enforce prior structural knowledge in deep structured learning, and 3) how to model certain special structures in medical image analytical problems. More specifically, we first introduce a formal formulation of structured learning in medical imaging and present a general deep structured learning framework to address this problem. Second, we enforce the prior structural knowledge in the loss function to further improve the analytical performance. Third, as an example of special structures in medical imaging, we introduce how to model the tree structures in coronary arteries with tree-structured convolutional long short-term memory. Finally, we further introduce a special structured learning problem in medical imaging which involves sequential decision making. Accordingly, a deep reinforcement learning-based solution is proposed. To put our discussion in the context of medical imaging, we evaluated our approaches on several medical image analytical tasks, i.e., cardiac recognizing from MRI sequences, metastasis detection in whole-slide images (WSIs), coronary artery segmentation from 3D computed tomography angiography (CTA) volumes, and axon tracing. The superior performance demonstrates the effectiveness of the proposed methods.
590 $a School code: 0694.
650 4 $a Medical imaging. $3 766168
650 4 $a Artificial intelligence. $3 194058
653 $a Medical image analysis
653 $a Structured learning
690 $a 0574
690 $a 0800
710 2 $a The University of North Carolina at Charlotte. $b Computer Science. $3 886515
773 0 $t Dissertations Abstracts International $g 81-10B.
790 $a 0694
791 $a Ph.D.
792 $a 2020
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=27666589