Pooling Image Datasets With Multiple Covariate Shift and Imbalance
ICLR 2024(2024)
摘要
Small sample sizes are common in many disciplines, which necessitates pooling
roughly similar datasets across multiple institutions to study weak but
relevant associations between images and disease outcomes. Such data often
manifest shift/imbalance in covariates (i.e., secondary non-imaging data).
Controlling for such nuisance variables is common within standard statistical
analysis, but the ideas do not directly apply to overparameterized models.
Consequently, recent work has shown how strategies from invariant
representation learning provides a meaningful starting point, but the current
repertoire of methods is limited to accounting for shifts/imbalances in just a
couple of covariates at a time. In this paper, we show how viewing this problem
from the perspective of Category theory provides a simple and effective
solution that completely avoids elaborate multi-stage training pipelines that
would otherwise be needed. We show the effectiveness of this approach via
extensive experiments on real datasets. Further, we discuss how this style of
formulation offers a unified perspective on at least 5+ distinct problem
settings, from self-supervised learning to matching problems in 3D
reconstruction.
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关键词
image harmonization,medical imaging
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