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GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10435)

Abstract

In voxel-based neuroimage analysis, lesion features have been the main focus in disease prediction due to their interpretability with respect to the related diseases. However, we observe that there exist another type of features introduced during the preprocessing steps and we call them “Procedural Bias”. Besides, such bias can be leveraged to improve classification accuracy. Nevertheless, most existing models suffer from either under-fit without considering procedural bias or poor interpretability without differentiating such bias from lesion ones. In this paper, a novel dual-task algorithm namely GSplit LBI is proposed to resolve this problem. By introducing an augmented variable enforced to be structural sparsity with a variable splitting term, the estimators for prediction and selecting lesion features can be optimized separately and mutually monitored by each other following an iterative scheme. Empirical experiments have been evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The advantage of proposed model is verified by improved stability of selected lesion features and better classification results.

Keywords

  • Voxel-based structural magnetic resonance imaging
  • Procedural bias
  • Split Linearized Bregman Iteration
  • Feature selection

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Notes

  1. 1.

    Here \(D_G:\mathbb {R}^V \rightarrow \mathbb {R}^E\) denotes a graph difference operator on \(G=(V,E)\), where V is the node set of voxels, E is the edge set of voxel pairs in neighbour (e.g. 3-by-3-by-3), such that \(D_G(\beta )(i,j):=\beta (i)-\beta (j)\).

  2. 2.

    http://adni.loni.ucla.edu.

  3. 3.

    For logit model, \(\alpha < \nu / \kappa (1 + \nu \Lambda _{H}^2 + \nu \Lambda _{X}^2)\) since \(\Lambda _{X} > \Lambda _{H}\).

  4. 4.

    In this experiment, comparable prediction result will be given for \(\nu \in (0.1,10)\).

  5. 5.

    0 corresponds to logistic regression model.

  6. 6.

    In [12], \(mDC := \frac{10 | \cap _{k=1}^{10} S(k) | }{\sum _{k=1}^{10} | S(k) |}\) where S(k) denotes the support set of \(\beta _{les}\) in k-th fold.

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Acknowledgements

This work was supported in part by 973-2015CB351800, 2015CB85600, 2012CB825501, NSFC-61625201, 61370004, 11421110001 and Scientific Research Common Program of Beijing Municipal Commission of Education (No. KM201610025013).

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Correspondence to Lingjing Hu or Yuan Yao .

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Sun, X., Hu, L., Yao, Y., Wang, Y. (2017). GSplit LBI: Taming the Procedural Bias in Neuroimaging for Disease Prediction. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D., Duchesne, S. (eds) Medical Image Computing and Computer Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science(), vol 10435. Springer, Cham. https://doi.org/10.1007/978-3-319-66179-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-66179-7_13

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