MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Breast Tumor Cellularity Assessment Using Deep Neural Networks

Author(s)
Rakhlin, Alexander; Tiulpin, Aleksei; Shvets, Alexey A; Kalinin, Alexandr A; Iglovikov, Vladimir I; Nikolenko, Sergey; ... Show more Show less
Thumbnail
DownloadSubmitted version (5.836Mb)
Open Access Policy

Open Access Policy

Creative Commons Attribution-Noncommercial-Share Alike

Terms of use
Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
Metadata
Show full item record
Abstract
© 2019 IEEE. Breast cancer is one of the main causes of death worldwide. Histopathological cellularity assessment of residual tumors in post-surgical tissues is used to analyze a tumor's response to a therapy. Correct cellularity assessment increases the chances of getting an appropriate treatment and facilitates the patient's survival. In current clinical practice, tumor cellularity is manually estimated by pathologists; this process is tedious and prone to errors or low agreement rates between assessors. In this work, we evaluated three strong novel Deep Learning-based approaches for automatic assessment of tumor cellularity from post-treated breast surgical specimens stained with hematoxylin and eosin. We validated the proposed methods on the BreastPathQ SPIE challenge dataset that consisted of 2395 image patches selected from whole slide images acquired from 64 patients. Compared to expert pathologist scoring, our best performing method yielded the Cohen's kappa coefficient of 0.69 (vs. 0.42 previously known in literature) and the intra-class correlation coefficient of 0.89 (vs. 0.83). Our results suggest that Deep Learning-based methods have a significant potential to alleviate the burden on pathologists, enhance the diagnostic workflow, and, thereby, facilitate better clinical outcomes in breast cancer treatment.
Date issued
2019
URI
https://hdl.handle.net/1721.1/138304
Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Journal
Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
Publisher
IEEE
Citation
Rakhlin, Alexander, Tiulpin, Aleksei, Shvets, Alexey A, Kalinin, Alexandr A, Iglovikov, Vladimir I et al. 2019. "Breast Tumor Cellularity Assessment Using Deep Neural Networks." Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019.
Version: Original manuscript

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.