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The Development and Optimization of a Deep-Learning Strategy for COVID-19 Classification in Chest X-Ray Radiography.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The Development and Optimization of a Deep-Learning Strategy for COVID-19 Classification in Chest X-Ray Radiography./
作者:	Griner, Dalton.
面頁冊數:	1 online resource (305 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Medical imaging. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30525435click for full text (PQDT)
ISBN:	9798379615550

The Development and Optimization of a Deep-Learning Strategy for COVID-19 Classification in Chest X-Ray Radiography.
Griner, Dalton.

The Development and Optimization of a Deep-Learning Strategy for COVID-19 Classification in Chest X-Ray Radiography. - 1 online resource (305 pages)

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

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2023.

Includes bibliographical references

This thesis scrutinizes the application of Artificial Intelligence (AI), specifically deep learning, in detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) or COVID-19 using Chest X-ray Radiography (CXR). It explores the development process of AI solutions for healthcare, with a focus on addressing the limitations and enhancing the generalizability of deep learning algorithms for COVID-19 detection through CXR. The study examines CXR as a cost-effective, portable, and readily available diagnostic tool, particularly during peak pandemic periods when PCR testing was insufficient. This study highlights the challenge of 'shortcut learning,' where the presence of hidden shortcuts or spurious correlations in training data affects model generalizability and develops methods to detect shortcut features present in datasets. This comprehensive analysis involves curating training data, designing and optimizing models, and evaluating their generalizability and interpretability. The study includes chapters detailing the clinical background of COVID-19, datasets utilized, investigation of shortcut learning, training and evaluation methods, model interpretability, and conclusions for future work in this area. The objective is to advance the integration of AI into clinical settings and improve the accuracy and speed of COVID-19 detection.

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

Mode of access: World Wide Web

ISBN: 9798379615550Subjects--Topical Terms:

3172799
Medical imaging.
Subjects--Index Terms:

Chest x-rayIndex Terms--Genre/Form:

542853
Electronic books.

The Development and Optimization of a Deep-Learning Strategy for COVID-19 Classification in Chest X-Ray Radiography.
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