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(Unpublished)
This is the latest version of this item.
Abstract
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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Details
Item Type: |
University of Pittsburgh ETD
|
Status: |
Unpublished |
Creators/Authors: |
|
ETD Committee: |
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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 |
URI: |
http://d-scholarship.pitt.edu/id/eprint/39053 |
Available Versions of this Item
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Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons. (deposited 05 Jun 2020 21:19)
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