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Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons

Falkenstein, Brian and Kovashka, Adriana and Chennubhotla, Chakra (2020) Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons. Master's Thesis, University of Pittsburgh. (Unpublished)

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In this study, we demonstrate the efficacy of medial axis derived scoring statistics for differentiating tumor and non-tumor nuclei in malignant breast tumor histopathology images. Characterizing nuclei shape is a crucial part of diagnosing breast tumors, and these scoring metrics may be integrated into algorithms which aggregate nuclei information across a region to label whole breast lesions. Nuclei and region scoring algorithms such as the one presented here can aid pathologists in the diagnosis of breast tumors.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Falkenstein, Brianbriankfalkenstein@gmail.combkf150000-0002-9629-1409
Kovashka, Adrianaadriana.kovashka@live.comkovashka0000-0003-1901-9660
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairKovashka, Adrianaadriana.kovashka@live.comkovashka0000-0003-1901-9660
Committee MemberHwang, Seong Jaesjh95@pitt.edusjh950000-0002-3713-5553
Committee MemberChakra,
Date: 5 June 2020
Date Type: Publication
Defense Date: 17 April 2020
Approval Date: 5 June 2020
Submission Date: 7 May 2020
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 19
Institution: University of Pittsburgh
Schools and Programs: School of Computing and Information > Computer Science
Degree: MS - Master of Science
Thesis Type: Master's Thesis
Refereed: Yes
Uncontrolled Keywords: Shape, breast cancer, machine learning, computer vision, skeleton, medial axis transform
Date Deposited: 05 Jun 2020 21:19
Last Modified: 05 Jun 2020 21:19

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  • Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons. (deposited 05 Jun 2020 21:19) [Currently Displayed]


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