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Abstract

A key component to the success of deep learning is the availability of massive amounts of training data. Building and annotating large datasets for solving medical image classification problems is today a bottleneck for many applications. Recently, capsule networks were proposed to deal with shortcomings of Convolutional Neural Networks (ConvNets). In this work, we compare the behavior of capsule networks against ConvNets under typical datasets constraints of medical image analysis, namely, small amounts of annotated data and class-imbalance. We evaluate our experiments on MNIST, Fashion-MNIST and medical (histological and retina images) publicly available datasets. Our results suggest that capsule networks can be trained with less amount of data for the same or better performance and are more robust to an imbalanced class distribution, which makes our approach very promising for the medical imaging community.

Keywords

Capsule networks Small datasets Class imbalance 

Notes

Acknowledgment

This work has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 713673. Amelia Jiménez-Sánchez has received financial support through the “la Caixa” INPhINIT Fellowship Grant for Doctoral studies at Spanish Research Centres of Excellence, “la Caixa” Banking Foundation, Barcelona, Spain. The authors would like to thank Nvidia for the GPU donation and Aurélien Geron for his tutorial and code on Capsule Networks.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.BCN MedTech, DTICUniversitat Pompeu FabraBarcelonaSpain
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  3. 3.Laboratoire des Sciences du Numérique de Nantes, UMR 6004, Centrale NantesNantesFrance

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