University of Oulu

A. Rakhlin, A. Tiulpin, A. A. Shvets, A. A. Kalinin, V. I. Iglovikov and S. Nikolenko, "Breast Tumor Cellularity Assessment Using Deep Neural Networks," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 371-380,

Breast tumor cellularity assessment using deep neural networks

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Author: Rakhlin, Alexander1; Tiulpin, Aleksei2; Shvets, Alexey A.3;
Organizations: 1Neuromation OU, Tallinn, 10111 Estonia
2University of Oulu, Oulu 90220, Finland
3MIT, Boston, MA 02142, USA
4University of Michigan, Ann Arbor, MI 48109, USA, San Francisco, CA 94107, USA
6Neuromation OU, Estonia, Steklov Mathematical Institute at St. Petersburg, Russia
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 5.8 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-06-03


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.

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Series: IEEE Conference on Computer Communications workshops
ISSN: 2159-4228
ISSN-L: 2159-4228
ISBN: 978-1-7281-5023-9
ISBN Print: 978-1-7281-5024-6
Pages: 371 - 380
DOI: 10.1109/ICCVW.2019.00048
Host publication: 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
Conference: IEEE/CVF International Conference on Computer Vision Workshop
Type of Publication: A4 Article in conference proceedings
Field of Science: 217 Medical engineering
Funding: The work of Sergey Nikolenko was supported by theRussian Foundation for Basic Research grant no. 18-54-74005.
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