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Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods./
Author:	Zhang, Haoyue.
Description:	1 online resource (157 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Medical imaging. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30522634click for full text (PQDT)
ISBN:	9798379610869

Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods.
Zhang, Haoyue.

Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods. - 1 online resource (157 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--University of California, Los Angeles, 2023.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379610869Subjects--Topical Terms:

3172799
Medical imaging.
Subjects--Index Terms:

Computer visionIndex Terms--Genre/Form:

542853
Electronic books.

Improving Acute Ischemic Stroke Diagnosis Using Medical Imaging and Deep Learning Methods.
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520 $a 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.
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