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Prognostic Power of Texture Based Morphological Operations in a Radiomics Study for Lung Cancer
The importance of radiomics features for predicting patient outcome is now well-established.
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
We propose a novel deep learning based approach for Prognostics with Uncertainty Quantification that is useful in scenarios where: (i) access to labeled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings.
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