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Multi-task fusion for improving mammography screening data classification
In this work, we propose a pipeline approach, where we first train a set of individual, task-specific models and subsequently investigate the fusion thereof, which is in contrast to the standard model ensembling strategy.
Domain aware medical image classifier interpretation by counterfactual impact analysis
Our solution identifies salient regions of an input image in a single forward-pass by measuring the effect of local image-perturbations on a predictor's score.
Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis
Clinical applicability of automated decision support systems depends on a robust, well-understood classification interpretation.
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