Advancement and Application of Deep Learning Techniques for Biomedical Image Analysis: Diagnostics, Risk, and Biomarker Prediction
Advancement and Application of Deep Learning Techniques for Biomedical Image Analysis: Diagnostics, Risk, and Biomarker Prediction
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152008
- ISBN
- 9798384022886
- DDC
- 574
- 저자명
- Leiby, Jacob.
- 서명/저자
- Advancement and Application of Deep Learning Techniques for Biomedical Image Analysis: Diagnostics, Risk, and Biomarker Prediction
- 발행사항
- [Sl] : University of Pennsylvania, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 114 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
- 주기사항
- Advisor: Kim, Dokyoon.
- 학위논문주기
- Thesis (Ph.D.)--University of Pennsylvania, 2024.
- 초록/해제
- 요약The advancement and application of deep learning techniques in the field of biomedical image analysis have experienced significant growth, driven by the ever-increasing sophistication of computational models and the availability of extensive imaging datasets. This dissertation presents an exploration into how deep learning can be leveraged to enhance diagnostic accuracy, risk stratification, and biomarker identification in various clinical contexts. Through a series of studies, we demonstrate the potential of deep learning frameworks to not only improve the classification of medical conditions-such as fatty liver disease and metabolic syndrome from abdominal imaging-but also to predict future disease risks, thereby facilitating early intervention strategies. Additionally, we show how integrating multiple learning strategies can improve biomarker prediction from histology whole slide images.In these investigations, deep learning models were trained to interpret complex imaging data, enabling the identification of subtle, often imperceptible patterns associated with pathological changes. The results underline the power of these models to surpass traditional imaging analysis techniques in both efficacy and efficiency. The findings underscore the transformative potential of deep learning in medical imaging, suggesting a shift towards more predictive, personalized healthcare.The integration of deep learning models into clinical practice promises not only to enhance diagnostic and prognostic capabilities but also to pave the way for advancements in precision medicine. Future directions are discussed, emphasizing the need for prospective longitudinal studies, integration into clinical workflows, and the increasing power of foundation models in the computational analysis of biomedical imaging.
- 일반주제명
- Bioinformatics
- 일반주제명
- Biomedical engineering
- 일반주제명
- Medical imaging
- 키워드
- Machine learning
- 키워드
- Deep learning
- 기타저자
- University of Pennsylvania Genomics and Computational Biology
- 기본자료저록
- Dissertations Abstracts International. 86-02B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798384022886
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■0820 ▼a574
■1001 ▼aLeiby, Jacob.
■24510▼aAdvancement and Application of Deep Learning Techniques for Biomedical Image Analysis: Diagnostics, Risk, and Biomarker Prediction
■260 ▼a[Sl]▼bUniversity of Pennsylvania▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a114 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-02, Section: B.
■500 ▼aAdvisor: Kim, Dokyoon.
■5021 ▼aThesis (Ph.D.)--University of Pennsylvania, 2024.
■520 ▼aThe advancement and application of deep learning techniques in the field of biomedical image analysis have experienced significant growth, driven by the ever-increasing sophistication of computational models and the availability of extensive imaging datasets. This dissertation presents an exploration into how deep learning can be leveraged to enhance diagnostic accuracy, risk stratification, and biomarker identification in various clinical contexts. Through a series of studies, we demonstrate the potential of deep learning frameworks to not only improve the classification of medical conditions-such as fatty liver disease and metabolic syndrome from abdominal imaging-but also to predict future disease risks, thereby facilitating early intervention strategies. Additionally, we show how integrating multiple learning strategies can improve biomarker prediction from histology whole slide images.In these investigations, deep learning models were trained to interpret complex imaging data, enabling the identification of subtle, often imperceptible patterns associated with pathological changes. The results underline the power of these models to surpass traditional imaging analysis techniques in both efficacy and efficiency. The findings underscore the transformative potential of deep learning in medical imaging, suggesting a shift towards more predictive, personalized healthcare.The integration of deep learning models into clinical practice promises not only to enhance diagnostic and prognostic capabilities but also to pave the way for advancements in precision medicine. Future directions are discussed, emphasizing the need for prospective longitudinal studies, integration into clinical workflows, and the increasing power of foundation models in the computational analysis of biomedical imaging.
■590 ▼aSchool code: 0175.
■650 4▼aBioinformatics
■650 4▼aBiomedical engineering
■650 4▼aMedical imaging
■653 ▼aBiomedical imaging
■653 ▼aBiomedical informatics
■653 ▼aMachine learning
■653 ▼aDeep learning
■653 ▼aPrecision medicine
■690 ▼a0715
■690 ▼a0541
■690 ▼a0574
■71020▼aUniversity of Pennsylvania▼bGenomics and Computational Biology.
■7730 ▼tDissertations Abstracts International▼g86-02B.
■790 ▼a0175
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17162398▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
