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Applying Medical Language Models to Medical Image Analysis
Applying Medical Language Models to Medical Image Analysis
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152018
- ISBN
- 9798382843001
- DDC
- 004
- 저자명
- Guo, Danfeng.
- 서명/저자
- Applying Medical Language Models to Medical Image Analysis
- 발행사항
- [Sl] : University of California, Los Angeles, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 111 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 85-12, Section: B.
- 주기사항
- Advisor: Terzopoulos, Demetri.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Los Angeles, 2024.
- 초록/해제
- 요약Medical image analysis powered by deep learning computer vision models has achieved significant advancements in the past decade. Deep learning models have demonstrated remarkable capabilities in a wide range of tasks, including medical image classification, detection, and segmentation. However, the limited availability of annotations has become a persistent challenge. Annotating medical images requires specialized professional knowledge, making it a costly process. This dissertation aims to relieve the reliance on medical image annotations by leveraging medical reports directly, which are usually associated with corresponding medical images and readily available. This thesis delves into the application of vision-language models, including large vision-language models, for enhancing medical image analysis. Existing vision-language models are modified and applied for three critical tasks: disease diagnosis, disease segmentation and medical report generation. In particular, the main contributions include: (1) proposing two prompting strategies to improve the accuracy of disease diagnosis through visual question answering in large vision language models; (2) introducing a disease segmentation model using medical reports as weak supervision; (3) evaluating medical large vision-language models in terms of the hallucination in generated reports across multiple complex diseases and applying existing techniques to mitigate the diagnostic errors in generated reports.
- 일반주제명
- Computer science
- 일반주제명
- Medical imaging
- 일반주제명
- Bioinformatics
- 키워드
- Computer vision
- 기타저자
- University of California, Los Angeles Computer Science 0201
- 기본자료저록
- Dissertations Abstracts International. 85-12B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798382843001
■035 ▼a(MiAaPQ)AAI31331952
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aGuo, Danfeng.
■24510▼aApplying Medical Language Models to Medical Image Analysis
■260 ▼a[Sl]▼bUniversity of California, Los Angeles▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a111 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 85-12, Section: B.
■500 ▼aAdvisor: Terzopoulos, Demetri.
■5021 ▼aThesis (Ph.D.)--University of California, Los Angeles, 2024.
■520 ▼aMedical image analysis powered by deep learning computer vision models has achieved significant advancements in the past decade. Deep learning models have demonstrated remarkable capabilities in a wide range of tasks, including medical image classification, detection, and segmentation. However, the limited availability of annotations has become a persistent challenge. Annotating medical images requires specialized professional knowledge, making it a costly process. This dissertation aims to relieve the reliance on medical image annotations by leveraging medical reports directly, which are usually associated with corresponding medical images and readily available. This thesis delves into the application of vision-language models, including large vision-language models, for enhancing medical image analysis. Existing vision-language models are modified and applied for three critical tasks: disease diagnosis, disease segmentation and medical report generation. In particular, the main contributions include: (1) proposing two prompting strategies to improve the accuracy of disease diagnosis through visual question answering in large vision language models; (2) introducing a disease segmentation model using medical reports as weak supervision; (3) evaluating medical large vision-language models in terms of the hallucination in generated reports across multiple complex diseases and applying existing techniques to mitigate the diagnostic errors in generated reports.
■590 ▼aSchool code: 0031.
■650 4▼aComputer science
■650 4▼aMedical imaging
■650 4▼aBioinformatics
■653 ▼aComputer vision
■653 ▼aImage segmentation
■653 ▼aLarge language models
■653 ▼aNatural language generation
■653 ▼aVisual question answering
■690 ▼a0800
■690 ▼a0984
■690 ▼a0574
■690 ▼a0715
■71020▼aUniversity of California, Los Angeles▼bComputer Science 0201.
■7730 ▼tDissertations Abstracts International▼g85-12B.
■790 ▼a0031
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162488▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
