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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.
Cohort Bias Adaptation in Aggregated Datasets for Lesion Segmentation
Many automatic machine learning models developed for focal pathology (e. g. lesions, tumours) detection and segmentation perform well, but do not generalize as well to new patient cohorts, impeding their widespread adoption into real clinical contexts.
Ontological attention ensembles for capturing semantic concepts in ICD code prediction from clinical text
We present a semantically interpretable system for automated ICD coding of clinical text documents.
