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Convolutional Neural Network-Based Classification of Histopathological Images Affected by Data Imbalance

  • Michał KoziarskiEmail author
  • Bogdan Kwolek
  • Bogusław Cyganek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11264)

Abstract

In this paper we experimentally evaluated the impact of data imbalance on the convolutional neural networks performance in the histopathological image recognition task. We conducted our analysis on the Breast Cancer Histopathological Database. We considered four phenomena associated with data imbalance: how does it affect classification performance, what strategies of preventing imbalance are suitable for histopathological data, how presence of imbalance affects the value of new observations, and whether sampling training data from a balanced distribution during data acquisition is beneficial if test data will remain imbalanced. The most important findings of our experimental analysis are the following: while high imbalance significantly affects the performance, for some of the metrics small imbalance. Sampling training data from a balanced distribution had a decremental effect, and we achieved a better performance applying a dedicated strategy of dealing with imbalance. Finally, not all of the traditional strategies of dealing with imbalance translate well to the histopathological image recognition setting.

Keywords

Convolutional neural network Data imbalance Histopathological image classification 

Notes

Acknowledgment

This research was supported by the National Science Centre, Poland, under the grant no. 2017/27/N/ST6/01705 and the PLGrid infrastructure.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michał Koziarski
    • 1
    Email author
  • Bogdan Kwolek
    • 1
  • Bogusław Cyganek
    • 1
  1. 1.Department of ElectronicsAGH University of Science and TechnologyKrakówPoland

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