Talk, Microsoft Visiting Data Science Educator Program (Summer 2022), Cohort 2, Remote
Supervised machine learning models are, by definition, data-sighted, requiring to view all or most parts of the training dataset which are labeled.
This paradigm presents two bottlenecks which are intertwined: risk of exposing sensitive data samples to the third-party site with machine learning engineers, and time-consuming, laborious, bias-prone nature of data annotations by the personnel at the data source site.
In this paper we studied learning impact of data adequacy as bias source in a data-blinded semi-supervised learning model for covid chest X-ray classification.
Data-blindedness was put in action on a semi-supervised generative adversarial network to generate synthetic data based only on a few labeled data samples and concurrently learn to classify targets.
We designed and developed a data-blind COVID–19 patient classifier that classifies whether an individual is suffering from COVID–19 or other type of illness with the ultimate goal of producing a system to assist in labeling large datasets.
However, the availability of the labels in the training data had an impact in the model performance, and when a new disease spreads, as it was COVID9-19 in 2019, access to labeled data may be limited.
Here, we studied how bias in the labeled sample distribution per class impacted in classification performance for three models: a Convolution Neural Network based classifier (CNN), a semi-supervised GAN using the source data (SGAN), and finally our proposed data-blinded semi-supervised GAN (BSGAN).
Data-blind prevents machine learning engineers from directly accessing the source data during training, thereby ensuring data confidentiality.
This was achieved by using synthetic data samples, generated by a separate generative model which were then used to train the proposed model.
Our model achieved comparable performance, with the trade–off between a privacy–aware model and a traditionally–learnt model of $0.05$ AUC–score, and it maintained stable, following the same learning performance as the data distribution was changed.