Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls
Biomedical imaging data presents enormous potential for deep learning models to predict invaluable properties, such as diseases and drug effects. However, unavoidable alterations of the technical conditions cause batch effects: variations between groups of samples that are not due to any biological signal of interest. Batch effects greatly hinder the generalization abilities of deep learning models, preventing their practical use in the real world. Unsupervised Domain Adaptation (UDA) methods have been proposed to mitigate batch effects, but they usually assume that the data is comprised of only one source domain and one target domain, whereas biological datasets are comprised of multiple...
| Authors | Ana Sanchez-Fernandez, Thomas Pinetz, Werner Zellinger et al. |
|---|---|
| Primary category | cs.LG |
| Published | 22 April 2026 |
| Last updated at source | 22 June 2026 |
Models and datasets referencing this paper
- anasanchezf/plate_effectsDataset
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