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

Development of Feature Extraction Mo...

Shi, Yu,

Linked to FindBook

Google Book

Amazon

博客來

Development of Feature Extraction Models to Improve Image Analysis Applications in Cancer /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of Feature Extraction Models to Improve Image Analysis Applications in Cancer // Yu Shi.
Author:	Shi, Yu,
Description:	1 electronic resource (169 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489788
ISBN:	9798383622483

Development of Feature Extraction Models to Improve Image Analysis Applications in Cancer /
Shi, Yu,

Development of Feature Extraction Models to Improve Image Analysis Applications in Cancer /Yu Shi. - 1 electronic resource (169 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these advancements, medical imaging analysis in cancer research and clinical settings faces significant challenges. Analyzing data produced by sophisticated imaging technologies, such as CT or MRI, is still labor-intensive, limiting its usability and contributing to disparities in cancer care and data hungriness for researchers. AI-assisted analysis has the potential not only to reduce cost and turnover time but also to increase the accuracy of clinical applications. Furthermore, it provides opportunities to integrate various types of data and information for better prediction, benefiting both patients and physicians.The research described in this dissertation aims to improve cancer imaging analysis by presenting the design and implementation of novel AI architectures. In this dissertation, I developed AI-based algorithms focused on two primary objectives. (1) Develop feature extraction methods to improve model accuracy. I applied advanced techniques to extract and learn critical image features associated with cancer prognosis to improve diagnostic tool accuracy and reliability. (2) Develop advanced generative models to synthesize high-quality image data. I developed deep-learning-based methods to learn latent representations and synthesize high-quality 3D images of tumor sites, facilitating better visualization and assessment of cancerous tissues.This dissertation showcases the immense potential of AI in revolutionizing cancer diagnostics, providing a foundation for further research and development in this critical healthcare field. The proposed AI frameworks, incorporating innovative applications of machine learning and deep learning methods, will undoubtedly drive ongoing efforts to reduce cancer worldwide and tackle major challenges in this area.

English

ISBN: 9798383622483Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Cancer imaging

Development of Feature Extraction Models to Improve Image Analysis Applications in Cancer /
LDR:03985nmm a22004453i 4500 001 2431323
005 20250603111429.5
006 m o d
007 cr|nu||||||||
008 261011s2024 miu||||||m |||||||eng d
020 $a 9798383622483
035 $a (MiAaPQD)AAI31489788
035 $a AAI31489788
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Shi, Yu, $e author. $3 3820070
245 1 0 $a Development of Feature Extraction Models to Improve Image Analysis Applications in Cancer / $c Yu Shi.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (169 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
500 $a Advisors: Zhang, Chi Committee members: Jain, Maneesh; Otu, Hasan; Moriyama, Etsuko; Clarke, Jennifer.
502 $b Ph.D. $c The University of Nebraska - Lincoln $d 2024.
520 $a Cancer poses a significant global health challenge. With an estimated 20 million new cases diagnosed worldwide in 2022 and 9.7 million fatalities attributable to the disease, the economic burden of cancer is immense. It impacts healthcare systems and imposes substantial costs for its care on patients and their families. Despite advancements in early detection, prevention, and treatment that have reduced overall cancer mortality rates, the growing prevalence of cancer, particularly among younger individuals, remains a pressing issue.Recent advancements in medical imaging technology have progressed significantly with the help of emerging computer vision and artificial intelligence (AI) technology. Despite these advancements, medical imaging analysis in cancer research and clinical settings faces significant challenges. Analyzing data produced by sophisticated imaging technologies, such as CT or MRI, is still labor-intensive, limiting its usability and contributing to disparities in cancer care and data hungriness for researchers. AI-assisted analysis has the potential not only to reduce cost and turnover time but also to increase the accuracy of clinical applications. Furthermore, it provides opportunities to integrate various types of data and information for better prediction, benefiting both patients and physicians.The research described in this dissertation aims to improve cancer imaging analysis by presenting the design and implementation of novel AI architectures. In this dissertation, I developed AI-based algorithms focused on two primary objectives. (1) Develop feature extraction methods to improve model accuracy. I applied advanced techniques to extract and learn critical image features associated with cancer prognosis to improve diagnostic tool accuracy and reliability. (2) Develop advanced generative models to synthesize high-quality image data. I developed deep-learning-based methods to learn latent representations and synthesize high-quality 3D images of tumor sites, facilitating better visualization and assessment of cancerous tissues.This dissertation showcases the immense potential of AI in revolutionizing cancer diagnostics, providing a foundation for further research and development in this critical healthcare field. The proposed AI frameworks, incorporating innovative applications of machine learning and deep learning methods, will undoubtedly drive ongoing efforts to reduce cancer worldwide and tackle major challenges in this area.
546 $a English
590 $a School code: 0138
650 4 $a Computer science. $3 523869
650 4 $a Medical imaging. $3 3172799
650 4 $a Oncology. $3 751006
653 $a Cancer imaging
653 $a Computer vision
653 $a Feature extraction
653 $a Generative AI
653 $a Medical imaging analysis
690 $a 0800
690 $a 0984
690 $a 0574
690 $a 0992
710 2 $a The University of Nebraska - Lincoln. $b Complex Biosystems. $e degree granting institution. $3 3820071
720 1 $a Zhang, Chi $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-02B.
790 $a 0138
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31489788