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International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2017: Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 pp 107–115Cite as

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).

Author information

Authors and Affiliations

  1. School of Mathematical Science, Peking University, Beijing, 100871, China

    Xinwei Sun

  2. Yanjing Medical College, Capital Medical University, Beijing, 101300, China

    Lingjing Hu

  3. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Yuan Yao

  4. Peking University, Beijing, China

    Yuan Yao

  5. National Engineering Laboratory for Video Technology, Key Laboratory of Machine Perception, School of EECS, Peking University, Beijing, 100871, China

    Yizhou Wang

Authors
  1. Xinwei Sun
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  2. Lingjing Hu
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  3. Yuan Yao
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  4. Yizhou Wang
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Corresponding authors

Correspondence to Lingjing Hu or Yuan Yao .

Editor information

Editors and Affiliations

  1. Université de Sherbrooke, Sherbrooke, QC, Canada

    Maxime Descoteaux

  2. DKFZ, Heidelberg, Germany

    Lena Maier-Hein

  3. Ulm University of Applied Sciences, Ulm, Germany

    Alfred Franz

  4. Université de Rennes 1, Rennes, France

    Pierre Jannin

  5. McGill University, Montreal, QC, Canada

    D. Louis Collins

  6. Université Laval, Québec, QC, Canada

    Dr. Simon Duchesne

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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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