# Batch Effect Mitigation

Cell morphology profiling has relied on cell segmentation and handcrafted features, which are annotation-hungry and hard to reuse across laboratories. Researchers pretrained four self-supervised models on 119,876 unannotated compounds and about 1.77 million JUMP–CP images, then froze the weights and extracted image embeddings directly. On 2,464 EU-OPENSCREEN Bioactives compounds, DINO and DINOv2 beat CellProfiler by 31–37% in site-averaged mean average precision for mechanism-of-action retrieval and consistently surpassed masked-based models.

Cell morphology profiling has relied on cell segmentation and handcrafted features, which are annotation-hungry and hard to reuse across laboratories. Researchers pretrained four self-supervised models on 119,876 unannotated compounds and about 1.77 million JUMP–CP images, then froze the weights and extracted image embeddings directly. On 2,464 EU-OPENSCREEN Bioactives compounds, DINO and DINOv2 beat CellProfiler by 31–37% in site-averaged mean average precision for mechanism-of-action retrieval and consistently surpassed masked-based models.

In glioma Raman spectra, a single spectrum is enough for a model to tell which patient it came from, a sample-specific bias unrelated to biology that can confound tumor typing. On the 46-sample APOLLO dataset, a learnable feature-importance layer is trained adversarially against a two-headed classifier to suppress patient-identity signal while keeping IDH mutation information. Patient-ID balanced accuracy falls from 68% to 12%, while mutant-versus-wild-type classification stays at 0.8 balanced accuracy.

In glioma Raman spectra, a single spectrum is enough for a model to tell which patient it came from, a sample-specific bias unrelated to biology that can confound tumor typing. On the 46-sample APOLLO dataset, a learnable feature-importance layer is trained adversarially against a two-headed classifier to suppress patient-identity signal while keeping IDH mutation information. Patient-ID balanced accuracy falls from 68% to 12%, while mutant-versus-wild-type classification stays at 0.8 balanced accuracy.