27 research outputs found
Is Texture Predictive for Age and Sex in Brain MRI?
Deep learning builds the foundation for many medical image analysis tasks
where neuralnetworks are often designed to have a large receptive field to
incorporate long spatialdependencies. Recent work has shown that large
receptive fields are not always necessaryfor computer vision tasks on natural
images. We explore whether this translates to certainmedical imaging tasks such
as age and sex prediction from a T1-weighted brain MRI scans.Comment: MIDL 2019 [arXiv:1907.08612
Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models
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 allows for estimating
the uncertainty for each treatment option and for the individual treatment
effects (ITE) between any two treatments. We train and evaluate this model to
predict future new and enlarging T2 lesion counts on a large, multi-center
dataset of MR brain images of patients with multiple sclerosis, exposed to
several treatments during randomized controlled trials. We evaluate the
correlation of the uncertainty estimate with the factual error, and, given the
lack of ground truth counterfactual outcomes, demonstrate how uncertainty for
the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate
how knowledge of uncertainty could modify clinical decision-making to improve
individual patient and clinical trial outcomes
Probabilistic and causal reasoning in deep learning for imaging
Typical machine learning research in the imaging domain occurs in clearly defined environments on clean datasets without considering realistic deployment scenarios. However, applied machine learning systems are exposed to unexpected distribution shifts and still need to produce reliable predictions without relying on spurious correlations. Similarly, such systems encounter ambiguous or unseen inputs and need to communicate their uncertainty. Often, AI systems support a human operator and should provide interpretable explanations of their decisions.
This thesis argues for a probabilistic and causal approach to machine learning that is robust to spurious correlations, improves interpretability, and communicates uncertainty.
First, we investigate the learning abilities of neural networks that are constrained to extracting information from image patches. We show that careful network design can prevent shortcut learning and that restricting the receptive field can improve the interpretability of predictions.
We tackle uncertainty estimation by introducing a Bayesian deep learning method to approximate the posterior distribution of the weights of a neural network using an implicit distribution. We verify that our method is capable of solving predictive tasks while providing reliable uncertainty estimates.
Moving on, we frame various medical prediction tasks within the framework of outlier detection. We apply deep generative modelling to brain MR and CT images as well as histopathology images and show that it is possible to detect pathologies as outliers under a normative model of healthy samples.
Next, we propose deep structural causal models as a framework capable of capturing causal relationships between imaging and non-imaging data. Our experiments provide evidence that this framework is capable of all rungs of the causal hierarchy.
Finally, with further thoughts on applications of uncertainty estimation, robust causal estimation, and fairness we conclude that the safe and reliable deployment of AI systems to real-world scenarios requires the integration of probabilistic and causal reasoning.Open Acces