An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning

The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability. In general, accurate deep-learning classifiers provide little model interpretability, whi...

FAIRNESS IN SEMI-SUPERVISED LEARNING: UNLABELED DATA HELP TO REDUCE DISCRIMINATION

Machine learning is widely deployed in society, unleashing its power in a wide rangeof applications owing to the advent of big data.One emerging problem faced by machine learning is the discrimination from data, and such discrimination is reflected in the eventual decisions made by the algorithms. Recent study has proved that increasing the size of...