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

Supporting Diagnosis of Pathologists...

Gu, Hongyan,

FindBook

Google Book

Amazon

博客來

Supporting Diagnosis of Pathologists With Human-AI Collaboration /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Supporting Diagnosis of Pathologists With Human-AI Collaboration // Hongyan Gu.
作者:	Gu, Hongyan,
面頁冊數:	1 electronic resource (235 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-09, Section: B.
Contained By:	Dissertations Abstracts International86-09B.
標題:	Computer science. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31842626
ISBN:	9798308121244

Supporting Diagnosis of Pathologists With Human-AI Collaboration /
Gu, Hongyan,

Supporting Diagnosis of Pathologists With Human-AI Collaboration /Hongyan Gu. - 1 electronic resource (235 pages)

Source: Dissertations Abstracts International, Volume: 86-09, Section: B.

The recent trend of digital pathology transition has enabled the advancement of Artificial Intelligence (AI) for complex pathology tasks. While some AI demonstrated performance comparable to human pathologists in lab studies, translating them into clinical practice remains challenging due to issues related to limitations of AI's integration into clinical decision-making, its explainability and controllability, and the reliability of AI-assisted outcomes.To address these challenges, this thesis adopts a multi-faceted approach, combining field investigations, artifact development, and empirical validation, to study effective human-AI collaborative paradigms in digital pathology. First, it presents findings from a field study of pathologists' daily workflows, their attitudes towards AI with varying levels of automation, and recommendations for designing effective AI-assisted diagnostic systems. Second, this thesis discusses the development and validation of NaviPath, a next-generation, high-throughput AI recommendation system informed by pathologists' domain expertise, and xPath, a comprehensive and explainable AI-assisted pathology interface that seamlessly integrates with pathologists' diagnostic tasks involving multiple criteria and multimodal data. Finally, this thesis explores strategies to foster appropriate reliance on AI by harnessing pathologists' collective expertise to achieve reliable, and robust AI-assisted outcomes.Overall, this thesis aspires to enable efficient, accurate, and safe human-AI collaborative pathology decisions -- supporting pathologists in reaching timely, cost-effective, and precise diagnoses, which can ultimately benefit patient management.

English

ISBN: 9798308121244Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Digital pathology

Supporting Diagnosis of Pathologists With Human-AI Collaboration /
LDR:03140nmm a22004453i 4500 001 2432632
005 20250731101900.5
006 m o d
007 cr|nu||||||||
008 261011s2025 miu||||||m |||||||eng d
020 $a 9798308121244
035 $a (MiAaPQD)AAI31842626
035 $a AAI31842626
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Gu, Hongyan, $e author. $3 3821927
245 1 0 $a Supporting Diagnosis of Pathologists With Human-AI Collaboration / $c Hongyan Gu.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2025
300 $a 1 electronic resource (235 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: 86-09, Section: B.
500 $a Advisors: Chen, Xiang Committee members: He, Lei; Arnold, Corey Wells; Yang, Lin.
502 $b Ph.D. $c University of California, Los Angeles $d 2025.
520 $a The recent trend of digital pathology transition has enabled the advancement of Artificial Intelligence (AI) for complex pathology tasks. While some AI demonstrated performance comparable to human pathologists in lab studies, translating them into clinical practice remains challenging due to issues related to limitations of AI's integration into clinical decision-making, its explainability and controllability, and the reliability of AI-assisted outcomes.To address these challenges, this thesis adopts a multi-faceted approach, combining field investigations, artifact development, and empirical validation, to study effective human-AI collaborative paradigms in digital pathology. First, it presents findings from a field study of pathologists' daily workflows, their attitudes towards AI with varying levels of automation, and recommendations for designing effective AI-assisted diagnostic systems. Second, this thesis discusses the development and validation of NaviPath, a next-generation, high-throughput AI recommendation system informed by pathologists' domain expertise, and xPath, a comprehensive and explainable AI-assisted pathology interface that seamlessly integrates with pathologists' diagnostic tasks involving multiple criteria and multimodal data. Finally, this thesis explores strategies to foster appropriate reliance on AI by harnessing pathologists' collective expertise to achieve reliable, and robust AI-assisted outcomes.Overall, this thesis aspires to enable efficient, accurate, and safe human-AI collaborative pathology decisions -- supporting pathologists in reaching timely, cost-effective, and precise diagnoses, which can ultimately benefit patient management.
546 $a English
590 $a School code: 0031
650 4 $a Computer science. $3 523869
650 4 $a Medical imaging. $3 3172799
650 4 $a Computer engineering. $3 621879
650 4 $a Electrical engineering. $3 649834
653 $a Digital pathology
653 $a Human pathologists
653 $a Decision-making
653 $a Multi-faceted approach
690 $a 0984
690 $a 0800
690 $a 0574
690 $a 0544
690 $a 0464
710 2 $a University of California, Los Angeles. $b Electrical and Computer Engineering 0333. $e degree granting institution. $3 3819920
720 1 $a Chen, Xiang $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-09B.
790 $a 0031
791 $a Ph.D.
792 $a 2025
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31842626