Classification of Breast Cancer Histopathological Images using Convolutional Neural Networks with Hierarchical Loss and Global Pooling

Zeya Wang, Nanqing Dong, Wei Dai, Sean D. Rosario, Eric P. Xing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

35 Scopus citations

Abstract

Deep learning-based computer-aided diagnosis (CAD) has been gaining popularity for analyzing histopathological images. However, there has been limited work that addresses the problem of accurately classifying breast biopsy tissue with hematoxylin and eosin stained images into different histological grades. We propose a system which can automatically classify breast cancer histology images into four classes, namely normal tissues, benign lesion, in situ carcinoma and invasive carcinoma. Our framework uses a Convolutional Neural Network (CNN) with a hierarchical loss, where failing to distinguish between carcinoma and non-carcinoma is penalized more than failing to distinguish between normal and benign or between in situ and invasive carcinoma. The network also includes a patch-wise design with global pooling directly on input images. By incorporating the hierarchical and global information of the input images, our framework can outperform the previous system by a large margin.

Original languageEnglish
Title of host publicationImage Analysis and Recognition - 15th International Conference, ICIAR 2018, Proceedings
EditorsBart ter Haar Romeny, Fakhri Karray, Aurelio Campilho
Pages745-753
Number of pages9
DOIs
StatePublished - 2018
Event15th International Conference on Image Analysis and Recognition, ICIAR 2018 - Povoa de Varzim, Portugal
Duration: Jun 27 2018Jun 29 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10882 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Image Analysis and Recognition, ICIAR 2018
Country/TerritoryPortugal
CityPovoa de Varzim
Period6/27/186/29/18

Bibliographical note

Publisher Copyright:
© 2018, Springer International Publishing AG, part of Springer Nature.

Keywords

  • Breast cancer
  • Convolutional Neural Networks
  • Hierarchical loss
  • Histopathology
  • Image classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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