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Publication Detail
Multi-stage prediction networks for data harmonization
  • Publication Type:
  • Authors:
    Blumberg SB, Palombo M, Khoo CS, Tax CMW, Tanno R, Alexander DC
  • Publisher:
  • Publication date:
  • Pagination:
    411, 419
  • Published proceedings:
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
  • Volume:
    11767 LNCS
  • ISBN-13:
  • Status:
  • Name of conference:
    Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference
  • Conference place:
    Shenzhen, China
  • Conference start date:
  • Conference finish date:
  • Print ISSN:
© Springer Nature Switzerland AG 2019. In this paper, we introduce multi-task learning (MTL) to data harmonization (DH); where we aim to harmonize images across different acquisition platforms and sites. This allows us to integrate information from multiple acquisitions and improve the predictive performance and learning efficiency of the harmonization model. Specifically, we introduce the Multi Stage Prediction (MSP) Network, a MTL framework that incorporates neural networks of potentially disparate architectures, trained for different individual acquisition platforms, into a larger architecture that is refined in unison. The MSP utilizes high-level features of single networks for individual tasks, as inputs of additional neural networks to inform the final prediction, therefore exploiting redundancy across tasks to make the most of limited training data. We validate our methods on a dMRI harmonization challenge dataset, where we predict three modern platform types, from one obtained from an old scanner. We show how MTL architectures, such as the MSP, produce around 20% improvement of patch-based mean-squared error over current state-of-the-art methods and that our MSP outperforms off-the-shelf MTL networks. Our code is available [1].
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