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Shihao Jin

Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network

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Oct 07, 2019
Shihao Jin, Nicolò Savioli, Antonio de Marvao, Timothy JW Dawes, Axel Gandy, Daniel Rueckert, Declan P O'Regan

Figure 1 for Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
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Figure 3 for Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network
Figure 4 for Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network

In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure. Different methods are evaluated to find the optimal way to insert conventional covariates into deep prediction networks. Correlation analysis between autoencoder latent codes and covariate features is used to examine how these predictors interact. We believe that similar approaches could also be used to introduce knowledge of genetic variants to such survival networks to improve outcome prediction by jointly analysing cardiac motion traits with inheritable risk factors.

* NeurIPS 2019, Medical Imaging meets NIPS  
* 4 pages, 2 figures 
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