東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Toward Explainability of Machine Learning in Medical Imaging: Generalizability, Separability, and Learnability = = 迈向医学影像中机器学习的可解释性：泛化性、可分离性和可学习性.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Toward Explainability of Machine Learning in Medical Imaging: Generalizability, Separability, and Learnability =/
Reminder of title:	迈向医学影像中机器学习的可解释性：泛化性、可分离性和可学习性.
Author:	Guan, Shuyue.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	312 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
Contained By:	Dissertations Abstracts International83-10B.
Subject:	Biomedical engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29064905
ISBN:	9798426801745

Toward Explainability of Machine Learning in Medical Imaging: Generalizability, Separability, and Learnability = = 迈向医学影像中机器学习的可解释性：泛化性、可分离性和可学习性.
Guan, Shuyue.

Toward Explainability of Machine Learning in Medical Imaging: Generalizability, Separability, and Learnability =迈向医学影像中机器学习的可解释性：泛化性、可分离性和可学习性. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 312 p.

Source: Dissertations Abstracts International, Volume: 83-10, Section: B.

Thesis (Ph.D.)--The George Washington University, 2022.

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

The applications of Deep Learning (DL) for medical imaging have become increasingly popular in recent years. During my studies of applications of Machine Learning (ML) and DL methods in medical imaging, I realized that there is a trade-off between accuracy and explainability for these methods. Although some DL methods have better performances, they are more difficult to understand and to explain. The lack of explainability limits the acceptance of DL applications by clinicians. The requirement of explainability and the DL applications for medical imaging that I have investigated thus have stimulated my research interest in eXplainable Artificial Intelligence (XAI). Explainability has multiple facets, and there is to date no unified definition. For explainable ML, I have primarily addressed these aspects: the separability of data, cluster validation, Generative Adversarial Network (GAN) evaluation, generalizability of the Deep Neural Network (DNN), learnability of DL models, and transparent DL. The study of explainable ML had been motivated by several completed applications for medical-object detection and segmentation. Studies of medical image analysis and the XAI contain very rich questions. My research aims to contribute to medical image analysis by focusing on the performance (accuracy) and explainability of applications, using ML and DL. The long-term goals of these works are to help make DL-based Computer-Aided Diagnosis (CAD) systems be transparent, understandable, and explainable and to win the trust of end-users; eventually, these new techniques can be widely accepted by clinicians to improve medical diagnosis and treatment outcomes.

ISBN: 9798426801745Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Explainable artificial intelligence

Toward Explainability of Machine Learning in Medical Imaging: Generalizability, Separability, and Learnability = = 迈向医学影像中机器学习的可解释性：泛化性、可分离性和可学习性.
LDR:04497nmm a2200409 4500 001 2352205
005 20221118093842.5
008 241004s2022 ||||||||||||||||| ||eng d
020 $a 9798426801745
035 $a (MiAaPQ)AAI29064905
035 $a AAI29064905
040 $a MiAaPQ $c MiAaPQ
100 1 $a Guan, Shuyue. $0 (orcid)0000-0002-3779-9368 $3 3691825
245 1 0 $a Toward Explainability of Machine Learning in Medical Imaging: Generalizability, Separability, and Learnability = $b 迈向医学影像中机器学习的可解释性：泛化性、可分离性和可学习性.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 312 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-10, Section: B.
500 $a Advisor: Loew, Murray H.
502 $a Thesis (Ph.D.)--The George Washington University, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a The applications of Deep Learning (DL) for medical imaging have become increasingly popular in recent years. During my studies of applications of Machine Learning (ML) and DL methods in medical imaging, I realized that there is a trade-off between accuracy and explainability for these methods. Although some DL methods have better performances, they are more difficult to understand and to explain. The lack of explainability limits the acceptance of DL applications by clinicians. The requirement of explainability and the DL applications for medical imaging that I have investigated thus have stimulated my research interest in eXplainable Artificial Intelligence (XAI). Explainability has multiple facets, and there is to date no unified definition. For explainable ML, I have primarily addressed these aspects: the separability of data, cluster validation, Generative Adversarial Network (GAN) evaluation, generalizability of the Deep Neural Network (DNN), learnability of DL models, and transparent DL. The study of explainable ML had been motivated by several completed applications for medical-object detection and segmentation. Studies of medical image analysis and the XAI contain very rich questions. My research aims to contribute to medical image analysis by focusing on the performance (accuracy) and explainability of applications, using ML and DL. The long-term goals of these works are to help make DL-based Computer-Aided Diagnosis (CAD) systems be transparent, understandable, and explainable and to win the trust of end-users; eventually, these new techniques can be widely accepted by clinicians to improve medical diagnosis and treatment outcomes.
520 $a 近年来，深度学习（DL）在医学影像中的应用越来越受到欢迎。在研究机器学习 (ML) 和 DL 方法在医学影像中的应用时，我意识到这些方法的准确性和可解释性之间存在相互权衡的关系。尽管某些 DL 方法具有更好的性能，但它们却更难以被理解和解释。可解释性的缺乏限制了临床医生对深度学习应用的接受。因此，我对于医学影像可解释性要求的调研和深度学习的应用激发了我对可解释人工智能 (XAI) 的研究兴趣。可解释性有多个方面，至今还没有统一的定义。对于可解释的 ML，我主要解决了关于：数据的可分离性、集群验证、生成对抗网络 (GAN) 评估、深度神经网络 (DNN) 的泛化性、DL 模型的可学习性和易于理解的 DL 方面的问题。可解释的机器学习的研究受到了几个已经完成了的关于医疗对象检测和分割的应用的推动。医学图像分析和 XAI 的研究包含非常丰富的问题。我的研究旨在通过关注 ML 和 DL 的应用效果（准确性）和可解释性，为医学图像分析方法的发展做出贡献。这些工作的长期目标是帮助使基于深度学习的计算机辅助诊断 (CAD) 系统变得透明、可被理解和解释，并赢得终端用户的信任；最终，这些新技术可以被临床医生们广泛接受，用以改善医疗诊断和治疗的效果。.
590 $a School code: 0075.
650 4 $a Biomedical engineering. $3 535387
650 4 $a Artificial intelligence. $3 516317
650 4 $a Computer science. $3 523869
650 4 $a Medical imaging. $3 3172799
653 $a Explainable artificial intelligence
653 $a Generalizability
653 $a Learnability
653 $a Machine learning
653 $a Medical image analysis
653 $a Separability
690 $a 0541
690 $a 0800
690 $a 0984
690 $a 0574
710 2 $a The George Washington University. $b Biomedical Engineering. $3 3544440
773 0 $t Dissertations Abstracts International $g 83-10B.
790 $a 0075
791 $a Ph.D.
792 $a 2022
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=29064905