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Exploration of Statistical Learning Strategies and Their Applications on Medical Image Data for Computer-Aided Diagnosis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Exploration of Statistical Learning Strategies and Their Applications on Medical Image Data for Computer-Aided Diagnosis./
作者:	Hu, Yifan.
面頁冊數:	1 online resource (78 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Applied mathematics. -
電子資源:	click for full text (PQDT)
ISBN:	9781369862812

Exploration of Statistical Learning Strategies and Their Applications on Medical Image Data for Computer-Aided Diagnosis.
Hu, Yifan.

Exploration of Statistical Learning Strategies and Their Applications on Medical Image Data for Computer-Aided Diagnosis. - 1 online resource (78 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Machine learning addresses the question of how computer make decisions and predictions automatically through existing experiences and data, which has become an increasingly important topic with the advent of modern data science and automated big data analysis. Several algorithms are widely used in machine learning. However, each classifier, inevitably, has certain inadequacy for which we hope to compensate. To address these issues, this study first introduces the necessary theoretical background and principles for machine learning and those typical classifiers. Based on these classifiers, this paper attempts to (1) use bagging/boosting to improve the simple classifier, and, (2) find some combination strategies to make use of the advantage of each classifier. The second part of this paper is to verify the robustness of these innovative ideas via multiple datasets. First, several common datasets are analyzed with the results compared between our new algorithm and those typical classifiers. Overall, we can obtain some gains in terms of the AUC value in virtually every dataset with the new algorithm and significant gains in most dataset. Secondly, we apply these algorithms to a real-life image data classification problem. The pipeline of this project includes 3D texture feature amplification, feature extraction via KL-transform, feature selection and classification. Finally, we gladly report that significant improvements have been achieved through both the new feature selection method and the new classification algorithm.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369862812Subjects--Topical Terms:

1069907
Applied mathematics.
Index Terms--Genre/Form:

554714
Electronic books.

Exploration of Statistical Learning Strategies and Their Applications on Medical Image Data for Computer-Aided Diagnosis.
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245 1 0 $a Exploration of Statistical Learning Strategies and Their Applications on Medical Image Data for Computer-Aided Diagnosis.
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Adviser: Wei Zhu.
502 $a Thesis (Ph.D.) $c State University of New York at Stony Brook $d 2017.
504 $a Includes bibliographical references
520 $a Machine learning addresses the question of how computer make decisions and predictions automatically through existing experiences and data, which has become an increasingly important topic with the advent of modern data science and automated big data analysis. Several algorithms are widely used in machine learning. However, each classifier, inevitably, has certain inadequacy for which we hope to compensate. To address these issues, this study first introduces the necessary theoretical background and principles for machine learning and those typical classifiers. Based on these classifiers, this paper attempts to (1) use bagging/boosting to improve the simple classifier, and, (2) find some combination strategies to make use of the advantage of each classifier. The second part of this paper is to verify the robustness of these innovative ideas via multiple datasets. First, several common datasets are analyzed with the results compared between our new algorithm and those typical classifiers. Overall, we can obtain some gains in terms of the AUC value in virtually every dataset with the new algorithm and significant gains in most dataset. Secondly, we apply these algorithms to a real-life image data classification problem. The pipeline of this project includes 3D texture feature amplification, feature extraction via KL-transform, feature selection and classification. Finally, we gladly report that significant improvements have been achieved through both the new feature selection method and the new classification algorithm.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Applied mathematics. $3 1069907
655 7 $a Electronic books. $2 local $3 554714
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710 2 $a State University of New York at Stony Brook. $b Applied Mathematics and Statistics. $3 1179106
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856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10252212 $z click for full text (PQDT)