소장자료
LDR | 03425nam 2200505 4500 | ||
001 | 0100803714▲ | ||
005 | 20240329141754▲ | ||
006 | m o d ▲ | ||
007 | cr#unu||||||||▲ | ||
008 | 240116s2023 us |||||||||||||||c||eng d▲ | ||
020 | ▼a9798379610869▲ | ||
035 | ▼a(MiAaPQ)AAI30522634▲ | ||
040 | ▼aMiAaPQ▼cMiAaPQ▲ | ||
082 | 0 | ▼a616▲ | |
100 | 1 | ▼aZhang, Haoyue.▲ | |
245 | 1 | 0 | ▼aImproving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods▼h[electronic resource]▲ |
260 | ▼a[S.l.]: ▼bUniversity of California, Los Angeles. ▼c2023▲ | ||
260 | 1 | ▼aAnn Arbor : ▼bProQuest Dissertations & Theses, ▼c2023▲ | |
300 | ▼a1 online resource(157 p.)▲ | ||
500 | ▼aSource: Dissertations Abstracts International, Volume: 84-12, Section: B.▲ | ||
500 | ▼aAdvisor: Arnold, Corey W.▲ | ||
502 | 1 | ▼aThesis (Ph.D.)--University of California, Los Angeles, 2023.▲ | |
506 | ▼aThis item must not be sold to any third party vendors.▲ | ||
520 | ▼aAcute ischemic stroke (AIS) is a cerebrovascular disease caused by deceased blood flow in the brain. Treatment of AIS is heavily dependent on the time since stroke onset (TSS), either by clock time or tissue time. AIS treatments aim to restore blood flow in the stroke-affected area to minimize infarction. Current clinical guidelines recommend thrombolytic therapies (e.g. Intravenous(IV) or Intra-arterial (IA) tissue Plasminogen Activator (tPA) for patients presenting within 4.5 hours of TSS and Mechanical Thrombectomy (MTB) (e.g. surgical removal of the clot) for patients with TSS up to 24 hours. This research attempts to use both CT and MRI to predict the eligibility of AIS patients and their response to treatment while addressing several challenges in neuroimaging and AIS diagnosis in clinical settings using novel machine learning and deep learning approaches. A Self-supervised Learning approach, called intra-domain task-adaptive transfer learning, is the first proposed to predict TSS using limited training data. A hybrid transformer model that utilizes spatial neighborhood information in brain regions is proposed to predict MTB success. A pure transformer and a specifically designed Masked Image Model are developed to predict Large Vessel Occlusion (LVO). Last, a transformer-based super-resolution framework is proposed to generate synthesized thin-slice images from thick-slice images. Together, these models demonstrate the effectiveness of the attention mechanism and the usefulness of self-supervised learning for clinical deep learning applications given the limited data resources compared to natural images.▲ | ||
590 | ▼aSchool code: 0031.▲ | ||
650 | 4 | ▼aMedical imaging.▲ | |
650 | 4 | ▼aBiomedical engineering.▲ | |
653 | ▼aComputer vision▲ | ||
653 | ▼aDeep learning▲ | ||
653 | ▼aMachine learning▲ | ||
653 | ▼aAcute ischemic stroke▲ | ||
653 | ▼aTime since stroke onset▲ | ||
653 | ▼aMechanical Thrombectomy▲ | ||
690 | ▼a0574▲ | ||
690 | ▼a0541▲ | ||
690 | ▼a0800▲ | ||
710 | 2 | 0 | ▼aUniversity of California, Los Angeles.▼bBioengineering 0288.▲ |
773 | 0 | ▼tDissertations Abstracts International▼g84-12B.▲ | |
773 | ▼tDissertation Abstract International▲ | ||
790 | ▼a0031▲ | ||
791 | ▼aPh.D.▲ | ||
792 | ▼a2023▲ | ||
793 | ▼aEnglish▲ | ||
856 | 4 | 0 | ▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T16932972▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.▲ |
Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods[electronic resource]
자료유형
국외eBook
서명/책임사항
Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods [electronic resource]
개인저자
발행사항
[S.l.] : University of California, Los Angeles. 2023 Ann Arbor : ProQuest Dissertations & Theses , 2023
형태사항
1 online resource(157 p.)
일반주기
Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Advisor: Arnold, Corey W.
Advisor: Arnold, Corey W.
학위논문주기
Thesis (Ph.D.)--University of California, Los Angeles, 2023.
요약주기
Acute ischemic stroke (AIS) is a cerebrovascular disease caused by deceased blood flow in the brain. Treatment of AIS is heavily dependent on the time since stroke onset (TSS), either by clock time or tissue time. AIS treatments aim to restore blood flow in the stroke-affected area to minimize infarction. Current clinical guidelines recommend thrombolytic therapies (e.g. Intravenous(IV) or Intra-arterial (IA) tissue Plasminogen Activator (tPA) for patients presenting within 4.5 hours of TSS and Mechanical Thrombectomy (MTB) (e.g. surgical removal of the clot) for patients with TSS up to 24 hours. This research attempts to use both CT and MRI to predict the eligibility of AIS patients and their response to treatment while addressing several challenges in neuroimaging and AIS diagnosis in clinical settings using novel machine learning and deep learning approaches. A Self-supervised Learning approach, called intra-domain task-adaptive transfer learning, is the first proposed to predict TSS using limited training data. A hybrid transformer model that utilizes spatial neighborhood information in brain regions is proposed to predict MTB success. A pure transformer and a specifically designed Masked Image Model are developed to predict Large Vessel Occlusion (LVO). Last, a transformer-based super-resolution framework is proposed to generate synthesized thin-slice images from thick-slice images. Together, these models demonstrate the effectiveness of the attention mechanism and the usefulness of self-supervised learning for clinical deep learning applications given the limited data resources compared to natural images.
주제
ISBN
9798379610869
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