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Dropout-Enabled Ensemble Learning for Multi-scale Biomedical Data

  • Alexandre MomeniEmail author
  • Marc ThibaultEmail author
  • Olivier GevaertEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11383)

Abstract

Leveraging information from multiple scales is crucial to understanding complex diseases such as cancer where this could have a significant impact in improving diagnoses, patient management and treatment decisions. Recent advances in Convolutional Neural Networks (CNNs) have enabled major breakthroughs in biomedical image analysis, in particular for histopathology and radiology images. Our main contribution is a methodology to combine independent CNN models built for these two types of images in order to improve diagnostic accuracy. We train separate CNN models and combine them using a Dropout-Enabled meta-classifier. Our framework achieved second place in the MICCAI 2018 Computational Precision Medicine Challenge.

Keywords

Biomedical imaging Cancer Computational Precision Medicine Deep learning 

Notes

Acknowledgements

Research reported in this publication was supported by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health under Award Number R01EB020527. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Departments of Medicine and Biomedical Data ScienceStanford UniversityStanfordUSA

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