Harmonization and the Worst Scanner Syndrome

01/15/2021
by   Daniel Moyer, et al.
0

We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable. If a predictive machine is made invariant to a set of domains, the accuracy of the output predictions (as measured by mutual information) is limited by the domain with the least amount of information to begin with. If a real label value is highly informative about the source domain, it cannot be accurately predicted by an invariant predictor. These results are simple and intuitive, but we believe that it is beneficial to state them for medical imaging harmonization.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/18/2023

DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification

The domain shift between training and testing data presents a significan...
research
07/05/2021

Causally Invariant Predictor with Shift-Robustness

This paper proposes an invariant causal predictor that is robust to dist...
research
06/11/2021

Invariant Information Bottleneck for Domain Generalization

The main challenge for domain generalization (DG) is to overcome the pot...
research
04/14/2023

Frequency Decomposition to Tap the Potential of Single Domain for Generalization

Domain generalization (DG), aiming at models able to work on multiple un...
research
07/19/2023

Spuriosity Didn't Kill the Classifier: Using Invariant Predictions to Harness Spurious Features

To avoid failures on out-of-distribution data, recent works have sought ...
research
05/24/2019

DIVA: Domain Invariant Variational Autoencoders

We consider the problem of domain generalization, namely, how to learn r...
research
09/06/2021

Optimal Prediction of Unmeasured Output from Measurable Outputs In LTI Systems

In this short article, we showcase the derivation of an optimal predicto...

Please sign up or login with your details

Forgot password? Click here to reset