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Debiasing Counterfactuals In the Presence of Spurious Correlations
Deep learning models can perform well in complex medical imaging classification tasks, even when basing their conclusions on spurious correlations (i. e. confounders), should they be prevalent in the training dataset, rather than on the causal image markers of interest.
Counterfactual Image Synthesis for Discovery of Personalized Predictive Image Markers
In this work, we demonstrate that data-driven biomarker discovery can be achieved through a counterfactual synthesis process.
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