Uncertainty quantification

[gcastro-98]

Implement model-agnostic uncertainity quantification procedures, such as according to this survey:

• Conformal Prediction (CP), it has been succesfully applied to:
  • Explain 99% of variance in predicted versus actual error for medical imaging classification
  • Improve audio-visual emotion classification for a semi-supervised GAN (compared with a similar network using the classifier alone)
• Monte Carlo dropout, as Bayesian approx. for Gaussian probabilistic processes (MC), it has been applied to:
  • To correlate uncertainity with observed error when estimating brain and cerebrospinal fluid intracellular volume applying CNN Qin et al.
  • To identify 96% of all predictions with high-risk for error (when applying a CNN for differentiating among glioma, multiple sclerosis, and healthy brain), Tanno et al.