Harmonization and the Worst Scanner Syndrome
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 11 of its 14 references that are in the map. First posted on arXiv 15 Jan 2021.
Builds on
11 of 14 references.
References · 11
- 2015Domain-Adversarial Training of Neural Networkscited by 3,023
- 2015Deep Learning and the Information Bottleneck Principlecited by 793
- 2019Domain Generalization via Model-Agnostic Learning of Semantic Featurescited by 405
- 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
- 2019DIVA: Domain Invariant Variational Autoencoderscited by 114
- 2018Invariant Representations without Adversarial Trainingcited by 111
- 2019Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentationcited by 50
- 2019Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effectscited by 40
- 2020A Learning Strategy for Contrast-agnostic MRI Segmentationcited by 35
- 2019Scanner Invariant Representations for Diffusion MRI Harmonizationcited by 27
- 2019Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRIcited by 12
Built on by
Citing papers per year: 2023: 1; 2024: 1.
2 papers.