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cs.LG2021★ 1 cited
Bayesian analysis of the prevalence bias: learning and predicting from imbalanced data
Loic Le Folgoc, Vasileios Baltatzis, Amir Alansary +8
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…
cs.LG2020
Bayesian Sampling Bias Correction: Training with the Right Loss Function
L. Le Folgoc, V. Baltatzis, A. Alansary +8
We derive a family of loss functions to train models in the presence of sampling bias. Examples are when the prevalence of a pathology differs from its sampling rate in the trainin…