01 Oct 21. Featured Paper
Probabilistic deep learning with adversarial training & volume interval estimation - better ways to perform & evaluate predictive models for white matter hyperintensities evolution.
Predicting disease progression always involves a high degree of uncertainty.
In predicting their progression previous studies have identified two main challenges: 1) uncertainty in predicting the areas/boundaries of shrinking & growing WMHs & 2) uncertainty in the estimation of future WMHs volume.
This study also proposes an evaluation procedure named volume interval estimation (VIE) for improving the interpretation of & confidence in the predictive deep learning model.
Our experiments show that the Probabilistic U-Net with adversarial training improved the performance of non-probabilistic U-Net in Dice similarity coefficient for predicting the areas of shrinking WMHs, growing WMHs, stable WMHs, & their average by up to 3.35%, 2.94%, 0.47%, & 1.03% respectively.
It also improved the volume estimation by 11.84% in the “Correct Prediction in Estimated Volume Interval” metric as per the newly proposed VIE evaluation procedure.
- Progression prediction
- Volume interval estimation
- White matter hyperintensities
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Featured paper: Probabilistic deep learning with adversarial training & volume interval estimation - better ways to perform & evaluate predictive models for white matter hyperintensities evolution