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Medical Note and Image Processing with Physical Models and Deep Learning Techniques.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Medical Note and Image Processing with Physical Models and Deep Learning Techniques./
作者:	Zhou, Hanyue.
面頁冊數:	1 online resource (142 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-09, Section: B.
Contained By:	Dissertations Abstracts International83-09B.
標題:	Artificial intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28970485click for full text (PQDT)
ISBN:	9798209887522

Medical Note and Image Processing with Physical Models and Deep Learning Techniques.
Zhou, Hanyue.

Medical Note and Image Processing with Physical Models and Deep Learning Techniques. - 1 online resource (142 pages)

Source: Dissertations Abstracts International, Volume: 83-09, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2022.

Includes bibliographical references

We aim to perform medical note comprehension and medical image processing, with an ultimate goal of cross-domain aggregation into a cooperative disease management system. The dissertation focuses on the initial technical development of each domain utilizing both physical modeling and deep learning methods. Medical notes and images taken from patients during their clinical visits are essential for patient care management. In this thesis, natural language processing techniques are developed for patient private information removal from medical reports, and image processing techniques are developed for semantic segmentation on different imaging modalities to achieve higher accuracy and enhanced structural integrity of the segmentation. Moreover, we demonstrate the importance of the manual labels used as the ground truth for supervised learning and assessment in the biomedical applications, and further propose a refinement scheme to improve label quality. Future directions would be integrating the complementary text and image information into a single robust system.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798209887522Subjects--Topical Terms:

516317
Artificial intelligence.
Subjects--Index Terms:

Deep learningIndex Terms--Genre/Form:

542853
Electronic books.

Medical Note and Image Processing with Physical Models and Deep Learning Techniques.
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504 $a Includes bibliographical references
520 $a We aim to perform medical note comprehension and medical image processing, with an ultimate goal of cross-domain aggregation into a cooperative disease management system. The dissertation focuses on the initial technical development of each domain utilizing both physical modeling and deep learning methods. Medical notes and images taken from patients during their clinical visits are essential for patient care management. In this thesis, natural language processing techniques are developed for patient private information removal from medical reports, and image processing techniques are developed for semantic segmentation on different imaging modalities to achieve higher accuracy and enhanced structural integrity of the segmentation. Moreover, we demonstrate the importance of the manual labels used as the ground truth for supervised learning and assessment in the biomedical applications, and further propose a refinement scheme to improve label quality. Future directions would be integrating the complementary text and image information into a single robust system.
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