Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Accurate ventricular volume measurements depend on complete heart coverage in cardiac magnetic resonance (CMR) from where most immediate indicators of normal/abnormal cardiac function are available non-invasively. However, incomplete coverage, especially missing basal or apical slices in CMR sequences is insufficiently addressed in population imaging and current clinical research studies yet has important impact on volume calculation accuracy. In this work, we propose a new deep architecture, coined Missing Slice Imputation Generative Adversarial Network (MSIGAN), to learn key features of cardiac short-axis (SAX) slices across different positions, and use them as conditional variables to effectively infer missing slices in the query volumes. In MSIGAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold conditional on slice intensity through a generator net. The latent vector along with the slice features (i.e., intensity) and desired position control the generation vs. regression. Two adversarial networks are imposed on the regressor and generator, encouraging more realistic slices. Experimental results show that our method outperforms the previous state-of-the-art in missing slice imputation for cardiac MRI

Estimating conditional density of missing values using deep Gaussian mixture model

We consider the problem of estimating the conditional probability distribution of missing values given the observed ones. We propose an approach, which combines the flexibility of deep neural networks with the simplicity of Gaussian mixture models (GMMs). Given an incomplete data point, our neural network returns the parameters of Gaussian distribution (in the form of Factor Analyzers model) representing the corresponding conditional density. We experimentally verify that our model provides better log-likelihood than conditional GMM trained in a typical way. Moreover, imputation obtained by replacing missing values using the mean vector of our model looks visually plausible.Comment: A preliminary version of this paper appeared as an extended abstract at the ICML 2020 Workshop on The Art of Learning with Missing Value

Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)

Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision in particular. Compared to such examples, however, there have been more limited applications of GANs to time series modelling, including forecasting. In this work, we present the Mixture Density Conditional Generative Adversarial Model (MD-CGAN), with a focus on time series forecasting. We show that our model is capable of estimating a probabilistic posterior distribution over forecasts and that, in comparison to a set of benchmark methods, the MD-CGAN model performs well, particularly in situations where noise is a significant component of the observed time series. Further, by using a Gaussian mixture model as the output distribution, MD-CGAN offers posterior predictions that are non-Gaussian.Comment: Revision includes further expansion of analysi