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Multi-source Information Gain for Random Forest: An Application to CT Image Prediction from MRI Data

  • Tri Huynh
  • Yaozong Gao
  • Jiayin Kang
  • Li Wang
  • Pei Zhang
  • Dinggang Shen
  • Alzheimer’s Disease Neuroimaging Initiative (ADNI)
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9352)

Abstract

Random forest has been widely recognized as one of the most powerful learning-based predictors in literature, with a broad range of applications in medical imaging. Notable efforts have been focused on enhancing the algorithm in multiple facets. In this paper, we present an original concept of multi-source information gain that escapes from the conventional notion inherent to random forest. We propose the idea of characterizing information gain in the training process by utilizing multiple beneficial sources of information, instead of the sole governing of prediction targets as conventionally known. We suggest the use of location and input image patches as the secondary sources of information for guiding the splitting process in random forest, and experiment on the challenging task of predicting CT images from MRI data. The experimentation is thoroughly analyzed in two datasets, i.e., human brain and prostate, with its performance further validated with the integration of auto-context model. Results prove that the multi-source information gain concept effectively helps better guide the training process with consistent improvement in prediction accuracy.

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© Springer International Publishing Switzerland 2015

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • Tri Huynh
    • 1
  • Yaozong Gao
    • 1
    • 2
  • Jiayin Kang
    • 1
  • Li Wang
    • 1
  • Pei Zhang
    • 1
  • Dinggang Shen
    • 1
  • Alzheimer’s Disease Neuroimaging Initiative (ADNI)
  1. 1.IDEA Lab, Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Computer ScienceUniversity of North Carolina at Chapel HillChapel HillUSA

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