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Found 4 papers, 3 papers with code
An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs
In order for these models to be safely deployed, we would like to ensure that they do not use confounding variables to make their classification, and that they will work well even when tested on images from hospitals that were not included in the training data.
Improving performance of deep learning models with axiomatic attribution priors and expected gradients
Recent research has demonstrated that feature attribution methods for deep networks can themselves be incorporated into training; these attribution priors optimize for a model whose attributions have certain desirable properties -- most frequently, that particular features are important or unimportant.
Explainable AI for Trees: From Local Explanations to Global Understanding
3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction.
Anesthesiologist-level forecasting of hypoxemia with only SpO2 data using deep learning
We also provide a simple way to visualize the reason why a patient's risk is low or high by assigning weight to the patient's past blood oxygen values.
