國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Discovering a Domain Knowledge Representation for Image Grouping :/
其他題名:	Multimodal Data Modeling, Fusion, and Interactive Learning.
作者:	Guo, Xuan.
面頁冊數:	1 online resource (204 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-12(E), Section: B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (204 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355104264Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Discovering a Domain Knowledge Representation for Image Grouping : = Multimodal Data Modeling, Fusion, and Interactive Learning.
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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.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Rochester Institute of Technology. $b Computing and Information Sciences. $3 1188646
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