# Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data

@article{Folgoc2021BayesianAO, title={Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data}, author={Lo{\"i}c Le Folgoc and Vasileios Baltatzis and Amir Alansary and Sujal Desai and Anand Devaraj and Sam Ellis and Octavio Martinez Manzanera and Fahdi Kanavati and Arjun Nair and Julia Anne Schnabel and Ben Glocker}, journal={ArXiv}, year={2021}, volume={abs/2108.00250} }

Datasets are rarely a realistic approximation of the target population. Say, prevalence is misrepresented, image quality is above clinical standards, etc. This mismatch is known as sampling bias. Sampling biases are a major hindrance for machine learning models. They cause significant gaps between model performance in the lab and in the real world. Our work is a solution to prevalence bias. Prevalence bias is the discrepancy between the prevalence of a pathology and its sampling rate in the…

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