Imperial College London
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Professor Anand Devaraj

Faculty of Medicine, National Heart & Lung Institute

Professor of Practice (Thoracic Radiology)
 
 
 
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Contact

 

anand.devaraj Website

 
 
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Location

 

South BlockRoyal Brompton Campus

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Summary

 

Publications

Citation

BibTex format

@unpublished{Folgoc:2021:10.48550/arXiv.2108.00250,
author = {Folgoc, LL and Baltatzis, V and Alansary, A and Desai, S and Devaraj, A and Ellis, S and Manzanera, OEM and Kanavati, F and Nair, A and Schnabel, J and Glocker, B},
doi = {10.48550/arXiv.2108.00250},
publisher = {ArXiv},
title = {Bayesian analysis of the prevalence bias: learning and predicting from  imbalanced data},
url = {http://dx.doi.org/10.48550/arXiv.2108.00250},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - UNPB
AB  - 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 hindrancefor machine learning models. They cause significant gaps between modelperformance in the lab and in the real world. Our work is a solution toprevalence bias. Prevalence bias is the discrepancy between the prevalence of apathology and its sampling rate in the training dataset, introduced uponcollecting data or due to the practioner rebalancing the training batches. Thispaper lays the theoretical and computational framework for training models, andfor prediction, in the presence of prevalence bias. Concretely a bias-correctedloss function, as well as bias-corrected predictive rules, are derived underthe principles of Bayesian risk minimization. The loss exhibits a directconnection to the information gain. It offers a principled alternative toheuristic training losses and complements test-time procedures based onselecting an operating point from summary curves. It integrates seamlessly inthe current paradigm of (deep) learning using stochastic backpropagation andnaturally with Bayesian models.
AU  - Folgoc,LL
AU  - Baltatzis,V
AU  - Alansary,A
AU  - Desai,S
AU  - Devaraj,A
AU  - Ellis,S
AU  - Manzanera,OEM
AU  - Kanavati,F
AU  - Nair,A
AU  - Schnabel,J
AU  - Glocker,B
DO  - 10.48550/arXiv.2108.00250
PB  - ArXiv
PY  - 2021///
TI  - Bayesian analysis of the prevalence bias: learning and predicting from  imbalanced data
UR  - http://dx.doi.org/10.48550/arXiv.2108.00250
UR  - http://arxiv.org/abs/2108.00250v1
UR  - http://hdl.handle.net/10044/1/96518
ER  -
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