東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Discovering a Domain Knowledge Repre...

Guo, Xuan.

FindBook

Google Book

Amazon

博客來

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning./
作者:	Guo, Xuan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	204 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
Contained By:	Dissertation Abstracts International78-12B(E).
標題:	Computer science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10603860
ISBN:	9780355104264

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning.
Guo, Xuan.

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 204 p.

Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.

Thesis (Ph.D.)--Rochester Institute of Technology, 2017.

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians' viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic.

ISBN: 9780355104264Subjects--Topical Terms:

523869
Computer science.

Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning.
LDR:03165nmm a2200325 4500 001 2160805
005 20180727125213.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355104264
035 $a (MiAaPQ)AAI10603860
035 $a (MiAaPQ)rit:12695
035 $a AAI10603860
040 $a MiAaPQ $c MiAaPQ
100 1 $a Guo, Xuan. $3 3348740
245 1 0 $a Discovering a Domain Knowledge Representation for Image Grouping: Multimodal Data Modeling, Fusion, and Interactive Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 204 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
500 $a Advisers: Anne Haake; Qi Yu.
502 $a Thesis (Ph.D.)--Rochester Institute of Technology, 2017.
520 $a In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians' viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic.
520 $a As a prior study, we conducted data elicitation experiments, where physicians were instructed to inspect each medical image towards a diagnosis while describing image content to a student seated nearby. Experts' eye movements and their verbal descriptions of the image content were recorded to capture various aspects of expert image understanding. This dissertation aims at an intuitive approach to extracting expert knowledge, which is to find patterns in expert data elicited from image-based diagnoses. These patterns are useful to understand both the characteristics of the medical images and the experts' cognitive reasoning processes.
520 $a The transformation from the viewed raw image features to interpretation as domain-specific concepts requires experts' domain knowledge and cognitive reasoning. This dissertation also approximates this transformation using a matrix factorization-based framework, which helps project multiple expert-derived data modalities to high-level abstractions.
520 $a To combine additional expert interventions with computational processing capabilities, an interactive machine learning paradigm is developed to treat experts as an integral part of the learning process. Specifically, experts refine medical image groups presented by the learned model locally, to incrementally re-learn the model globally. This paradigm avoids the onerous expert annotations for model training, while aligning the learned model with experts' sense-making.
590 $a School code: 0465.
650 4 $a Computer science. $3 523869
690 $a 0984
710 2 $a Rochester Institute of Technology. $b Computing and Information Sciences. $3 1064735
773 0 $t Dissertation Abstracts International $g 78-12B(E).
790 $a 0465
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10603860