A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data

@article{Plsterl2019AWA,
  title={A Wide and Deep Neural Network for Survival Analysis from Anatomical Shape and Tabular Clinical Data},
  author={S. P{\"o}lsterl and I. Sarasua and B. Guti{\'e}rrez-Becker and C. Wachinger},
  journal={ArXiv},
  year={2019},
  volume={abs/1909.03890}
}
  • S. Pölsterl, I. Sarasua, +1 author C. Wachinger
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • We introduce a wide and deep neural network for prediction of progression from patients with mild cognitive impairment to Alzheimer's disease. Information from anatomical shape and tabular clinical data (demographics, biomarkers) are fused in a single neural network. The network is invariant to shape transformations and avoids the need to identify point correspondences between shapes. To account for right censored time-to-event data, i.e., when it is only known that a patient did not develop… CONTINUE READING
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    References

    SHOWING 1-10 OF 65 REFERENCES
    Predicting Alzheimer’s disease progression using multi-modal deep learning approach
    • 38
    • PDF
    Domain adaptation for Alzheimer's disease diagnostics
    • 62
    • PDF
    Latent Representation Learning for Alzheimer’s Disease Diagnosis With Incomplete Multi-Modality Neuroimaging and Genetic Data
    • 24
    • PDF
    Automated MRI measures predict progression to Alzheimer's disease
    • 80
    • PDF
    Whole-brain analysis reveals increased neuroanatomical asymmetries in dementia for hippocampus and amygdala.
    • 59
    • PDF
    Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model
    • 34
    • PDF