Abstract: Exploring Sparsity in CNNs for Medical Image Segmentation BRIEFnet

Conference paper
Part of the Informatik aktuell book series (INFORMAT)

Zusammenfassung

Deep convolutional neural networks can evidently achieve astonishing accuracies for multiple medical image analysis tasks, in particular segmentation and detection. However, the actual translation of deep learning into clinical practice is so far very limited, in part because their extensive computations rely on specialised GPU hardware that is not easily available in clinical environments.

Literatur

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    Heinrich MP, Oktay O. BRIEFnet: Deep pancreas segmentation using binary sparse convolutions. In: Proc MICCAI. Springer; 2017. p. 329–337.Google Scholar
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    Xu Z, Lee CP, et al. Evaluation of six registration methods for the human abdomen on clinically acquired CT. IEEE Trans Biomed Eng. 2016;63(8):1563–1572.Google Scholar

Copyright information

© Springer-Verlag GmbH Deutschland 2018

Authors and Affiliations

  1. 1.Institut für Medizinische InformatikUniversität zu LübeckLübeckDeutschland
  2. 2.Biomedical Image Analysis GroupImperial College LondonLondonUK

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