Semantic Segmentation of Nuclei from Breast Histopathological Images by Incorporating Attention in U-Net

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Breast cancer is a major disease in the world and is detected by histopathological image analysis. The structure and characteristics of nuclei contributes largely in the decision of malignancy of a tumor. There exists several medical image processing techniques based on traditional and CNN methods to segment nuclei from breast histopathological images. However, these algorithms use hand crafted features and depend on availability of large annotated dataset. Moreover, heterogeneous structure and characteristic of nuclei makes it non trivial task. In this context, this paper presents an encoder decoder based CNN architecture to semantically segment nuclei from breast histopathological images. A new attention mechanism is used to extract feature from the nuclei regions at multiple scales. The proposed architecture is evaluated on breast histopathological images and achieved an mIoU of 0.77.

Original languageEnglish
Title of host publicationComputer Vision and Image Processing - 5th International Conference, CVIP 2020, Revised Selected Papers
EditorsSatish Kumar Singh, Partha Roy, Balasubramanian Raman, P. Nagabhushan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages137-148
Number of pages12
ISBN (Print)9789811610851
DOIs
Publication statusPublished - 2021
Event5th International Conference on Computer Vision and Image Processing, CVIP 2020 - Prayagraj, India
Duration: 04-12-202006-12-2020

Publication series

NameCommunications in Computer and Information Science
Volume1376 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference5th International Conference on Computer Vision and Image Processing, CVIP 2020
Country/TerritoryIndia
CityPrayagraj
Period04-12-2006-12-20

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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