An Ensemble-based Active Learning for Breast Cancer Classification

Sanghoon Lee, Mohamed Amgad, Mohamed Masoud, Rajasekaran Subramanian, David Gutman, Lee Cooper

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

3 Scopus citations

Abstract

Ensembled machine learning paradigms enable base learners to provide more accurate predictions than a standard approach using a single learner. Though the ensemble learning decreases variance or bias, improving predictions, limited literatures have been reported with an active learning strategy narrowing uncertainty in prediction. We present an ensemble based active learning approach for breast cancer detection, averaging predictions from the start of the art machine learning models on histopathology images. We demonstrate that the ensemble based active learning approach outperforms other approaches on breast cancer detection.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
EditorsIllhoi Yoo, Jinbo Bi, Xiaohua Tony Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2549-2553
Number of pages5
ISBN (Electronic)9781728118673
DOIs
StatePublished - Nov 2019
Event2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019 - San Diego, United States
Duration: Nov 18 2019Nov 21 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019

Conference

Conference2019 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2019
CountryUnited States
CitySan Diego
Period11/18/1911/21/19

Keywords

  • active learning
  • breast cancer
  • ensemble learning
  • machine learning
  • whole slide images

ASJC Scopus subject areas

  • Biochemistry
  • Biotechnology
  • Molecular Medicine
  • Modeling and Simulation
  • Health Informatics
  • Pharmacology (medical)
  • Public Health, Environmental and Occupational Health

Fingerprint Dive into the research topics of 'An Ensemble-based Active Learning for Breast Cancer Classification'. Together they form a unique fingerprint.

Cite this