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035 a| (Sirsi) o1450451758;

035 a| (OCoLC)1450451758;

049 a| EREE;

090 a| RC280.B8;

100 1 a| Rose, Madison,e| author.=| ^A1483899;

245 10 a| Patch based analysis with machine learning to aid breast cancer recurrence prediction /c| by Madison Rose.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2024.;

300 a| 1 online resource (57 pages) :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 1.34 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2024.;

502 a| Presented to the Faculty of the Department of Computer Science;

500 a| Advisor: Nic Herndon;

500 a| Title from PDF t.p. (viewed August 1, 2025).;

520 3 a| Since the introduction of whole slide scanners, machine learning research has become a popular area of interest in digital pathology. Many studies have attempted to use machine learning to aid pathology tasks such as breast cancer diagnosis and metastasis detection. However, one area that has less available research is in applying machine learning to predict patient recurrence risk categories. Since H&E-stained images are routinely collected for diagnostic purposes, creating an image-based recurrence prediction method could help increase accessibility and lower cost for recurrence risk category assessment for breast cancer patients. In this study, patches were extracted from a dataset of 102 whole slide images to train a machine learning model to predict slide level breast cancer Oncotype DX risk category using only H&E-stained images with no additional clinical data or region of interest annotations. Multiple patch size and patch quantity combinations were tested. Patches were extracted from each whole slide image and feature extraction was performed before the features were aggregated together to create a bag of features for each case. These bags were then used to train a logistic regression model. The best scoring model utilized 2,000 patches of size 256 x 256 pixels. This model scored 0.628 ± 0.044 accuracy on 5-fold cross validation across the entire dataset.;

504 a| Includes bibliographical references.;

650 0 a| Breastx| Cancerx| Imaging.=| ^A1239503;

650 0 a| Breastx| Cancerx| Prognosis.=| ^A1239513;

650 0 a| Machine learning.=| ^A145211;

650 0 a| Image processing.=| ^A115874;

700 1 a| Herndon, Nic,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/13392;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 6462775;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462775-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 7/31/2024x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462775-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 7/31/2024x| ETDz| HERESOURCE;

Patch based analysis with machine learning to aid breast cancer recurrence prediction / by Madison Rose.

Author/creator	Rose, Madison author.
Other author	Herndon, Nic, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2024.
Description	1 online resource (57 pages) : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Breast--Cancer. --Imaging. Breast--Cancer. --Prognosis. Machine learning. Image processing.

Summary	Since the introduction of whole slide scanners, machine learning research has become a popular area of interest in digital pathology. Many studies have attempted to use machine learning to aid pathology tasks such as breast cancer diagnosis and metastasis detection. However, one area that has less available research is in applying machine learning to predict patient recurrence risk categories. Since H&E-stained images are routinely collected for diagnostic purposes, creating an image-based recurrence prediction method could help increase accessibility and lower cost for recurrence risk category assessment for breast cancer patients. In this study, patches were extracted from a dataset of 102 whole slide images to train a machine learning model to predict slide level breast cancer Oncotype DX risk category using only H&E-stained images with no additional clinical data or region of interest annotations. Multiple patch size and patch quantity combinations were tested. Patches were extracted from each whole slide image and feature extraction was performed before the features were aggregated together to create a bag of features for each case. These bags were then used to train a logistic regression model. The best scoring model utilized 2,000 patches of size 256 x 256 pixels. This model scored 0.628 ± 0.044 accuracy on 5-fold cross validation across the entire dataset.
General note	Advisor: Nic Herndon
General note	Title from PDF t.p. (viewed August 1, 2025).
Dissertation note	M.S. East Carolina University 2024.
Dissertation note	Presented to the Faculty of the Department of Computer Science
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Patch based analysis with machine learning to aid breast cancer recurrence prediction / by Madison Rose.

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