TY - GEN
T1 - A wide and deep neural network for survival analysis from anatomical shape and tabular clinical data
AU - Pölsterl, Sebastian
AU - Sarasua, Ignacio
AU - Gutiérrez-Becker, Benjamín
AU - Wachinger, Christian
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - 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 Alzheimer’s disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient’s hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer’s Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.
AB - 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 Alzheimer’s disease up to a particular time point, we employ a loss commonly used in survival analysis. Our network is trained end-to-end to combine information from a patient’s hippocampus shape and clinical biomarkers. Our experiments on data from the Alzheimer’s Disease Neuroimaging Initiative demonstrate that our proposed model is able to learn a shape descriptor that augments clinical biomarkers and outperforms a deep neural network on shape alone and a linear model on common clinical biomarkers.
UR - http://www.scopus.com/inward/record.url?scp=85083734255&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-43823-4_37
DO - 10.1007/978-3-030-43823-4_37
M3 - Conference contribution
AN - SCOPUS:85083734255
SN - 9783030438227
T3 - Communications in Computer and Information Science
SP - 453
EP - 464
BT - Machine Learning and Knowledge Discovery in Databases - International Workshops of ECML PKDD 2019, Proceedings
A2 - Cellier, Peggy
A2 - Driessens, Kurt
PB - Springer
T2 - 19th Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2019
Y2 - 16 September 2019 through 20 September 2019
ER -