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Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models. (arXiv:2305.03829v4 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
Image-based precision medicine aims to personalize treatment decisions based
on an individual's unique imaging features so as to improve their clinical
outcome. Machine learning frameworks that integrate uncertainty estimation as
part of their treatment recommendations would be safer and more reliable.
However, little work has been done in adapting uncertainty estimation
techniques and validation metrics for precision medicine. In this paper, we use
Bayesian deep learning for estimating the posterior distribution over factual
and counterfactual outcomes on several treatments. This …
arxiv decisions features frameworks image imaging machine machine learning medicine part precision precision medicine recommendations treatment uncertainty work